Hey buddies! Welcome to the next tutorial on deep learning, in which you are about to acquire knowledge related to Python. This is going to be very interesting because the connection between these two is easy and useful. In the last lecture, we had an eye on the latest and trendiest deep learning algorithms, and therefore, I think you are ready to take the next step towards the implementation of the information that I shared with you. To help you make up your mind about the topics of today, I have made a list for you that will surely be useful for you to understand what we are going to do today.
How do you introduce the Python programming language to a deep learning developer?
How is Python useful for deep learning training in different ways?
Do Python provide the useful frameworks for the depe learning?
What are some libraries of Python that are useful for the deep learning?
Why do programmers prefer Python over other options when working with deep learning?
What are some other options besides Python to be used with deep learning?
Over the years, the hot topic in the world of programming languages has been Python because of many reasons that you will learn soon. It is critical to understand that when selecting a coding language, you must always be confident in its efficiency and functionality. Python is the most popular because of its fantastic performance, and therefore, I have chosen it for this course. From 2017 to the present, calculations and estimations of popularity show that Python is in the top ten in the interests of both common users and professionals due to its ease of installation and unrivaled efficiency.
Now, recall that deep learning is a popular topic in the industry of science and technology, and people are working hard to achieve their goals with the help of deep learning because of its jaw-dropping results. When talking about complexity, you will find that deep learning is a difficult yet useful field, and therefore, to minimize the complexity, experts recommend python as the programming language. All the points discussed below are an extraction of my personal experience, and I chose the best points that every developer must know. The following is a list of the points that will be discussed next:
I am discussing this point at the start of the discussion because I think it is one of the most important points that make programming better and more effective. If the code is clean and easy to read, you will definitely be able to pay attention to the programming in a better way. Usually, the programming is done in the grouping phase, and for the testing and other phases of successful programming, it is important to understand the code written by the others. Hence, coding in Python is easy to read and understand, and by the same token, you will be able to share and practice more and more with this interesting coding language.
The syntax and rules of the Python programming language allow you to present your code without mentioning many details. People realize that it is very close to the human language, and therefore, there is no need to have a lot of practice or knowledge to start practising. These are the important points that prove the importance of the Python language for writing more useful code. As a result, you can conclude that for complex and time taking processes such as deep learning, Python is one of the ideal languages because you do not have to spend a lot of time coding but will be able to use this energy to understand the concepts of deep learning and its applications.
Python, like other modern programming languages, supports a variety of programming paradigms. It fully supports:
Object-oriented Programming
Structured programming
Furthermore, its language features support a wide range of concepts in functional and aspect-oriented programming. Another point that is important to notice is that Python also includes a dynamic type system and automatic memory management.
Python's programming paradigms and language features enable you to create large and complex software applications. Therefore, it is a great language to use with deep learning.
If you are a programmer, you will have the idea that for different programming languages, you have to download and install other platforms for proper working. It becomes hectic to learn, buy, and use other platforms for the working of a single language. But when talking about Python, the flexibility can be proven by looking at the following points:
It supports multiple operating systems.
It is an interpreted programming language. That means you can run the Python code on several platforms without the headache of recompilation for the other platforms.
The testing of the Python code is easier than in some other programming languages.
All these points are enough to understand the best combination of deep learning with the Python programming language because deep learning requires the training and testing process, and there may be a need to test the same code or the network on different platforms.
Want to know why Python is better than other programming languages? One of the major reasons is the fantastic and gigantic library of the Python language. It is a programming tip that programmers should always check the programming language's library if they want to know its efficiency and ability to work instantly. One thing to notice is that you will get a large number of modules, and it allows you to choose the modules of your choice. So, you can ignore the unnecessary modules. This feature is also present in other popular programming languages. Moreover, you can also add more code according to your needs. For the experts, it is a blessing because they can use their creativity by using the already-saved modules.
Deep Elarnigna only contains algorithms, and it requires a programming language that allows for simple and quick module creation. Python is therefore ideal for deep embedding in context.
In the past lectures, we have seen the frameworks of deep learning, and therefore, for the best compatibility, the programming language in which the deep learning is being processed must also have open-source frameworks; otherwise, this advantage of deep learning will not be useful. Most of the time, the tools and frameworks are not only open source but also easily accessible, which makes your work easier. I believe that having more coding options makes your work easier because coding is a time-consuming process that requires you to have as much ease as possible for better practice. Here is the list of some frameworks that are used with the Python programming language:
Django
Flask
Pyramid
Bottle
Cherrypy.
Another reason why experts recommend Python for deep learning is the Python frameworks related to graphical user interfaces. In the previous lectures, you have seen that deep learning has a major application in image and video processing, and therefore, it is a good match for deep learning with Python coding. The GUI frameworks of Python include:
PyQT
PyJs
PyGUI
Kivy
PyGTK
WxPython
Observe that the keyword "Py" with all these frameworks indicates the specification of the Python programming language with these frameworks. At this point, it is not important to understand all of them. But as an example, I want to tell you that Kivy is used for the front end of Android apps with the help of Python.
This category makes it important to notice the connection between the Python programming language and deep learning because, when working with deep learning, a greater variety of frameworks results in an easier working and better training process.
If you are following our previous tutorials, you will be aware of the importance of testing in deep learning. But allow me to tell you the connection between Python and the test-driven approach. In deep learning, all efficiency depends upon the testing process. More and more training and testing means better performance, which the network can recognize better. Python provides for the rapid creation of prototype applications, and similarly, it also provides the best test driven approach when connected to networks.
The first rule to learning programming languages is to have consistency in your nature. Yet, for the more difficult programming languages, where the absence of a single semicolon can be confusing for the compiler, consistency is difficult to attain. On the contrary, an easier and more readable programming language, such as Python, helps to pay more attention to the code, and thus the user is more drawn to its work. Deep learning can only be performed in such an environment. So, for peace of mind, always choose Python.
Have you ever been stuck in a problem while coding and could not find the help you needed? I've seen this many times, and it's a miserable situation because the code contains your hard work from hours or even days, but you still have to leave it. Yet, because of the popularity and saturation of this field, Python developers are not alone. Python is a comparatively easy language, and normally people do not face any major issues. Yet, for the help of the developers, there is a large community related to Python where you can find the solution of your problems, check the trends, have a chit chat with other developers, etc.
When working on deep learning projects, it's fun to be a part of a community with other people who are working on similar projects. It is the perfect way to learn from the seniors and grow in a productive environment. Moreover, while you are solving the problems of the juniors, you will cultivate creativity in your mind, and deep learning will become interesting for you.
At this point, where I am discussing a lot about Python, it must be clarified that it is not the only option for deep learning. Deep learning subjects are always wasteful, and users always have more than one option. However, we prefer Python for a variety of reasons, and now I'd like to tell you about some other options that appear useful but are, in fact, less useful than Python. The other programming languages are:
JavaScript
Swift
Ruby
R.
C
C++
Julia
PHP
No doubt, people are showing amazing results when they combine one or more of these programming languages with deep learning, but usually, I prefer to work more with Python. It totally depends on the type of project you have or other parameters such as the algorithm, frameworks, hardware the user has, etc. to effectively choose the best programming language for deep learning. An expert always has an eye on all the parameters and then chooses the perfect way to solve the deep learning problems, no matter what the difficulty level of the language is.
Hence, we have discussed a lot about the Python today. Before all this discussion, our focus was on the deep learning and its working so you amy have the idea what actually si going on. In this article, we have seen the compatibility of the Python programming language with deep learning. We knew about the parameters of the deep learning and therefore were able to understand the reason of choosing the Python for our work. Throughout this article, we have seen different reasons why we have chosen TensorFlow and related libraries for our work. It is important to notice that Python works best with the TensorFlow and Keras APIs, and therefore, from day one, we have focused on both of these. In the next lecture, you will see some more important information about deep learning, and we are moving towards the practical implementation of this information. Once we have performed the experiment, all the points will be crystal clear in your mind. So until then, learn with us and grow your knowledge.
Hello pupils! Welcome to the following lecture on deep learning. As we move forward, we are learning about many of the latest and trendiest tools and techniques, and this course is becoming more interesting. In the previous lecture, you saw some important frameworks in deep learning, and this time, I am here to introduce you to some fantastic algorithms of deep learning that are not only important to understand before going into the practical implementation of the deep learning frameworks but are also interesting to understand the applications of deep learning and related fields. So, get ready to learn the magical algorithms that are making deep learning so effective and cool. Yet before going into details, let me discuss the questions for which we are trying to find answers.
How does deep learning algorithms are introduced?
What is the working of deep learning algorithms?
What are some types of DL algorithms?
How do deep learning algorithms work?
How these algorithms are different from each other?
Deep learning plays an important role in the recognition of objects and therefore, people use this feature in image, video and voice recognition where the objects are not only detected but can be changed, removed, edited, or altered using different techniques. The purpose to discuss these algorithms with you is, to have more and more knowledge and practice to choose the perfect algorithm for you and to have the concept of the efficiency and working of each algorithm. Moreover, we will discuss the application to provide you with the idea to make new projects by merging two or more algorithms together or creating your own algorithm.
Throughout this course, you are learning that with the help of the implementation of deep learning, computers are trained in such a way that they can take human-like decisions and can have the ability to act like humans with the help of their own intelligence. Yet, it is time to learn about how they are doing this and what the core reason is behind the success of these intelligent computers.
First of all, keep in mind that deep learning is done in different layers, and these layers are run with the help of the algorithm. We introduce the deep learning algorithm as:
“Deep learning algorithms are the set of instructions that are designed dynamically to run on the several layers of neural networks (depending upon the complexity of the neural networks) and are responsible for running all the data on the pre-trained decision-making neural networks.”
One must know that, usually, in machine learning, there is tough training to work with complex datasets that have hundreds of columns or features. This becomes difficult with the classic deep learning algorithm, so the developers are constantly designing a more powerful algorithm with the help of experimentation and research.
When people are using different types of neural networks with the help of deep learning, they have to learn several algorithms to understand the working of each layer of the algorithm. Basically, these algorithms depend upon the ANNs (artificial neural networks) that follow the principles of human brains to train the network.
While the training of the neural network is carried out, these algorithms take the unknown data as input and use it for the following purposes:
To group the objects
To extract the required features
To find out the usage patterns of data
The basic purpose of these algorithms is to build different types of models. There are several algorithms for neural networks, and it is considered that no algorithm is perfect for all types of tasks. All of them have their own pros and cons, and to have mastery over the deep learning algorithm, you have to study more and more and test several algorithms in different ways.
Do you remember that in the previous lectures we discussed the types of deep learning networks? Now you will observe that, while discussing the deep learning algorithms, you will utilize your concepts of neural networks. With the advancement of deep learning concepts, several algorithms are being introduced every year. So, have a look at the list of algorithms.
Convolutional Neural Networks (CNNs)
Long Short-Term Memory Networks (LSTMs)
Deep Belief Networks (DBNs)
Generative Adversarial Networks (GANs)
Autoencoders
Radial Basis Function Networks (RBFNs)
Multilayer Perceptrons (MLPs)
Restricted Boltzmann Machines( RBMs)
Recurrent Neural Networks (RNNs)
Self-Organizing Maps (SOMs)
Do not worry because we are not going to discuss all of them at a time but will discuss only the important ones to give you an overview of the networks.
Convolutional neural networks are also known as "ConvNets," and the main applications of these networks are in image processing and related fields. If we look back at its history, we find that it was first introduced in 1998. Yan LeCun initially referred to it as LeNet. At that time, it was introduced to recognize ZIP codes and other such characters.
We know that neural networks have many layers, and similar is the case with CNN. We observe different layers in this type of network, and these are described below:
Sr # |
Name of the Layer |
Description of the Layer |
1 |
Convolution layer |
The convolution layer contains many filters and is used to perform the convolution operations. |
2 |
Rectified linear unit |
The short form of this layer is ReLu, and it is used to perform different operations on the elements. It is called “rectified” because the output is obtained as a rectified feature map by using this layer. |
3 |
Pooling layer |
This is the layer where the results of the ReLu are fed as the input. Pooling is defined as the downsampling operation, and it is used to reduce the dimension of a feature map. The next phase is to convert this feature map, and then this two-dimensional array is converted into a single flat, continuous, and single vector. |
4 |
Fully connected layer |
The single vector from the pooling layer is finally fed into this last layer. At the end, classification of the image is done to identify it. |
As a reminder, you must know that neural networks have many layers, and the output of one layer becomes the input for the next layer. In this way, we get refined and better results in every layer.
This is a type of RNN (recurrent neural network) with a good memory that is used by experts to remember long-term dependencies. By default, it has the ability to recall past information over a long period of time. Because of this ability, LSTMs are used in time series prediction. It is not a single layer but a combination of four layers that communicate with each other in a unique way. Some very typical uses of LSTM are given below:
Speech recognition
Development in pharmaceutical operations
Different compositions in music
If you are familiar with the fundamentals of programming, you will understand that if we want to repeat a process, loops, or recurrent processes, are the solution. Similarly, the recurrent neural network is the one that forms the directed cycles. The unique thing about it is that the output of the LSTM becomes the input of the RNN. It means these are connected in a sequence, and in this way, the current phase becomes the output of the LSTM.
The main reason why this connection is magical is that you can utilize the feature of memory storage in LSTM and the ability of RNNs to work in a cyclic way. Some uses of RNN are given next:
Recognition of handwriting
Time series analysis
Translation by the machine
Natural language processing
captioning the images
The output of the RNN is obtained by following the equation given next:
If
output=t-1
Then
input=1
So at the output t
input=1+1
And this series goes on
Moreover, RNN can be used with any length of the input, but the size of the model does not increase when the input size is increased.
Next on the list is the GAN or the generative adversarial network. These are known as “adversarial networks" because they use two networks that compete with each other to generate real-time synthesized data. It is one of the major reasons why we found applications of the generative adversarial network in video, image, and voice generation.
GANs were first described in a paper published in 2014 by Ian Goodfellow and other researchers at the University of Montreal, including Yoshua Bengio. Yann LeCun, Facebook's AI research director, referred to GANs as "the most interesting idea in ML in the last 10 years." This made GANs a popular and interesting neural network. Another reason why I like this network is the fantastic feature of mimicking. You can create music, voice, video, or any related application that is difficult to recognize as being made by a machine. The impressive results are making this network more and more popular every day, but the evil of this network is equal. As with all technologies, people can use them for negative purposes, so check and balance are applied to such techniques. Moreover, GAN can generate realistic images and cartoons with high-quality results and render 3D objects.
At first, the network learns to distinguish between the generated fake data and sampled data. It happens when fake data is produced and the discriminator learns to recognise if it is real or false. After that, GAN is responsible to send the results to the generator so that it can learn and memorize the results and go for further training.
If it seems a simple and easy task, then think again because the recognition part is a tough job and you have to feed the perfect data in the perfect way so you may have accurate results every time.
For the problems in the function approximation, we use an artificial intelligence technique called the radial basis function network. It is somehow a little bit different from the previous ones. These are the types of forward-feed neural networks, and the speed and performance of these networks make them better than the other neural networks. These are highly efficient and have a better learning speed than others, but they require experience and hard work. Another reason to call them better is the presence of only one hidden layer and one radial basis function that is used as an activation function. Keep in mind that the activation function is highly efficient in its approximation of the results.
It takes the data from a training set and measures the similarities in the input. In this way, it classifies the data.
In the layer of RBF neurons, the input vector is then fed into the input layer.
After finding the weighted sum of the inputs, we obtain the output. Each category or class of data has one node.
The difference from the other network is, the neurons contain a gaussian transfer function, and the output is inversely proportional to the distance between the centre of the network and the neuron.
In the end, we get the output, which is a combination of both, the input of the radial basis function and the neuron parameters.
So, it seems that these networks are enough for today. Although there are different types of neural networks as well, as we have said earlier, with the advancement in deep learning, more and more algorithms for the neural networks are being introduced that have their own specifications, yet at this level, we just wanted to give you an idea about the neural networks. At the start of this article, we have seen what deep learning algorithms are and how they are different from other types of algorithms. We have seen the types of neural networks that include CNNs, LSTMNs, RNNs, GANs, and RBFs.
Hello peeps. Welcome to the next tutorial on deep learning. You have learned about the neural network, and it was an interesting way to compare different types of neural networks. Now, we are talking about deep learning frameworks. In the previous sessions, we introduced you to some important frameworks to let you know about the connection of different entities, but at this level, it is not enough. We are telling you in detail about all types of frameworks that are in style because of their latest features. So before we start, have a look at the list of concepts that will be covered today:
Introduction to the frameworks of deep learning.
Why do we require frameworks in deep learning?
What are some important deep learning frameworks?
What is TensorFlow and for which purpose of using TensorBoard?
Why Keras is famous?
What is the relationship between python and PyTorch?
How can we choose the best framework?
Deep learning is a complex field of machine learning, and it is important to have command over different types of tools and tricks so that you may design, train, and understand several types of neural networks efficiently with the minimum amount of time. Frameworks are used in many different types of programming languages, and this is the software that, by combining different tools, improves and simplifies the operation of the programming language.
The best thing about the frameworks is that they allow you to train their models without knowing or bothering about the algorithms that are running behind the programming. Isn’t it amazing to know that you will get a helping hand to understand and train your model without any worries? Once you know much about the different frameworks, it will be clear to you how these frameworks do some specific types of tasks to make your training process easy and interesting.
In the beginning, when you start the programming of the deep learning process by hand, you will see some interesting results related to your task. Yet, when you move towards complex tasks or when you are at the intermediate level, you will realize that it is strenuous and time-consuming to perform a simple task at a higher level. Moreover, the repetition of the same code can sometimes make you sick.
Usually, the need for a framework arises when you start working with advanced neural networks such as convolutional neural networks, or simply CNN, where the involvement of images and video makes the task difficult and time-consuming. These frameworks have pre-defined types of networks and also provide you with an easy way to access a great deal of information.
With the advancement of deep learning, many organizations are working to make it more user-friendly so that more people can use it for advanced technologies. It is one of the reasons behind the popularity of deep learning that a great deal of deep learning frameworks is introduced every year. We have analyzed different platforms and researched different reports. We found some amazing frameworks, and our experts have been checking them for a long time to provide you with the best framework for your learning. Here is the list of the frameworks that we will discuss in detail with you, along with the pros and cons of each.
Tensorflow
Keras
PyTorch
Theano
DL4J
Lasagna
Caffe
Chainer
We are not going to discuss all of them because it may be confusing for you to understand all the frameworks. Moreover, we believe in smart working, and therefore, we are simply discussing the most popular frameworks so that you may learn the way to compare different parameters, and after that, you will get the perfect way to make modules, train, and test the projects in different ways for smart working.
The first framework to be discussed here is TensorFlow, which is undoubtedly the most popular framework for deep learning because of its easy availability and great performance. The backbone of this platform is directly connected to Google’s brain team, which has represented it for deep learning and provided easy access to almost all types of users. It supports Python and some other programming languages, and the good thing about it is that it also works with dataflows. This point makes it more useful because, when dealing with different types of neural networks, it is extremely useful to understand the progress and the efficiency of your model.
Another important point to notice about TensorFlow is, it creates models that are undemanding to build and contain robust.
A plus point about this framework is another large package called TensorBoard. There are several advantages to this fabulous data package, but some of them are listed below:
The basic working of this package is to provide data visualization to the user, which is a great step for the ease of the user, but unfortunately, people are less aware of this, although it is a useful item.
Another advantage of tensorBoard is that it makes the sharing of the data with the shareholders easy and comfortable because of its fantastic data display.
You can use different packages with the help of TensorBoard.
You can get other basic information about TensorFlow by paying attention to the following table:
TensorFlow |
|
Releasing Dates |
November 9, 2015, and January 21, 2021. |
Programming Languages |
Python, C++, CUDA |
category |
Library of machine learning |
Name of Platforms |
JavaScript, Linux, Windows, Android, macOS, |
License |
Apache License 2.0 |
Website’s Link |
|
The next on the list is another famous and useful library for deep learning that most of you may know about. Keras is one of the favourite frameworks for deep learning developers because of its demand and open-source contributors. An estimate says that 35,000+ users are making this platform more and more popular.
Keras is written in the Python programming language, and it can support high-level neural networks. You must keep in mind that Keras is an API, and it runs on top of highly popular libraries such as TensorFlow and Theano. You will see this in action in our coming lectures. Because of its user-friendly features, Keras is used by a large number of companies as a startup and is a great tool for researchers and students.
The most prominent feature of Keras is its user-friendly nature. It seems that the developers have presented this framework to all types of users, no matter if they are professionals or learners. If users encounter an error or issue, they should receive transparent and actionable feedback.
For me, modularity is a useful feature because it makes tasks easier and faster. Moreover, the errors are easily detectable, which is a big relief. The modularity is shown with a graphical representation or sequence of information so that the user may understand it well.
Here's some good news for researchers and students. Keras is one of the best options for researchers because it allows them to make their own modules and test them according to their choice. Adding the modules to your project is super easy on Keras, and you can do advanced research without any issues.
Keras |
|
Releasing Dates |
March 27, 2015, and June 17, 2020. |
Programming Languages |
N/A |
category |
Almost all types of neural networks |
Name of Platforms |
Cross-platforms |
License |
Massachusetts Institute of Technology (MIT) |
Website’s Link |
|
Our next topic of discussion is PyTorch. It is another open-source library for deep learning and is used to build complex neural networks in an easy way. The thing that attracted me to this library is the platform that introduced it. It is developed under the umbrella of Facebook's AI Research Lab. I'm curious about how powerful it is because every time I open my Facebook app, I find the content I've chosen and wished for. People have been using it for deep learning, computer vision, and other related purposes since 2016, as it is a free open source for AI and related fields. By using PyTorch with other powerful libraries such as NumPy, Tensor, etc., you can build, train, and test complex neural networks. Because of its easy accessibility, PyTorch is popular among people. The versatility of the programming languages and different libraries working with PyTorch is another reason for its success.
A feature that makes it easy to use is its hybrid front-end nature, which makes it faster and more flexible to use. The user-friendly nature of this library makes it the perfect choice for professionally non-technical people.
With the help of its torch-distributed backend, you can have optimal performance all the time and keep an eye on the training and working of the network you are using. It has a powerful architecture, and on an advanced level, you can use it for complex neural networks.
As you can guess, PyTorch is run with the help of Python, which is one of the most popular and trending programming languages, and the plus point is that it allows many libraries to be used with it and work on neural networks.
PyTorch |
|
Releasing Dates |
September 2016, and December 10, 2020. |
Category |
Machine learning library, Deep learning library |
Name of Platforms |
Cross-platforms |
License |
Berkeley Software Distribution (BSD) |
Website’s Link |
|
Since now, we have been talking about the frameworks, and the basic purpose of discussing different features was to tell you the difference between them. A beginner may believe that all frameworks are the same, but this is incorrect because each framework has its own specialities and the difficulty level of using them varies. So, if you want to work perfectly in your field, first you must learn how to choose the best framework for your task. Keep in mind, these are not the only points that you need to know; all the parameters change according to the complexity of your project.
Not all projects are the same. You do not have to use the same framework every time. You must know more than one framework and choose one according to your needs. For example, for simple tasks, there is no need to use a complex framework or a higher-level neural network. There is versatility in the projects in deep learning, and you have to understand the needs of your project every time before choosing your required framework. As a result, before you begin, you should ask yourself the following questions about the project:
What are you using? Modern deep learning framework or are you interested in the classic ML algorithms?
What is your preferred programming language for the AI modules?
For the process of scaling, which type of hardware and software do you have for the working?
Once you know the different features of the frameworks, you may get the answers to all the questions given above.
Machine learning is a vast field, and with the advancement of different techniques, there is always a need to compare the parameters all the time. Different algorithms follow different types of parameters, and you must know all of them while choosing your framework. Moreover, you must also know if you are going with the classic built-in machine-learning algorithms or want to create your own.
Hence, we learned a lot about the frameworks of deep learning today. It was an interesting lecture where we saw the detailed introduction of the framework and compared TensorFlow, PyTorch, and Keras by discussing several features and requirements of all these frameworks. We will see all the discussion in action in the coming lectures. The purpose of this session was to clear the concept of working and variations in the framework and in this way, you have the idea how deep learning is useful in different ways. Researcher are working in deep learning and it is one of the basic reason behind the develorpment of different frameworks.
Hello Learners! Welcome to the next lecture on deep learning. We have read the detailed introduction to deep learning and are moving forward with the introduction of the neural network. I am excited to tell you about the neural network because of the interesting and fantastic applications of neural networks in real life. Here are the topics of today that will be covered in this lecture:
What do we mean by the neural network?
How can we know about the structure of the neural network?
What are the basic types of neural networks?
What are some applications of these networks?
Give an example of a case where we are implementing neural networks.
Artificial intelligence has numerous features that make it special and magical in different ways, and we will be exploring many of them in different ways in this course. So, first of all, let us start with the introduction.
Have you ever observed that your favorite videos are shown to you on Facebook or other social media platforms? Or does the advertisement for the product you are searching for pop up when using the phone applications? All of these are because of the artificial intelligence of the system that is running in the backend of the app and we have discussed it many times before.
To understand well about the neural network, let us discuss the inspiration and the model that has resulted in the formation of the neural network. We all have the idea of the neural network of the human brain. We are the best creation because of the complex and the best brain that calculates, estimate, and predict the results of the repeating processes in a better way. The same is the case with the neural network of computer systems. We have discussed the basic structure of the neural network many times but now, it's time to know about the structure of the neural network.
I always wonder how the answering software and apps such as Siri reply to us accurately and without any delay. The answer to this question was found in the workings and architecture of the network working behind this beautiful voice. I do not want to start the biology class here, but for proper understanding, I have to discuss the process when we hear a voice and understand it through the brain.
When we hear a sound in the surroundings, it is first caught by the ear, and this raw audio is acting as an input for the nerve of the ear. These nerves then pass this signal to the next layers that in return, pass these signals further to the next layers.
The layer makes the result more refined and accurate. Finally, the last layer reaches the brain where the brain makes the decision to respond. The same process is used in the neural network. This statement will be clear to you how it works, but for that, you have to know about the seven types of neural networks.
Feed Forward Neural Network
Recurrent Neural Network
Radial Basis Function (RBF) Neural Network
Convolution Neural Network
Modular Neural Network
Kohonen Self-organizing Neural Network
Multi-Layer Perception
Let me start with the very basic type of neural network so that you may understand it slowly and gradually. The workings of this network are related to its name. The motion of the information or the nerves is unidirectional, and the process is ended in the output. In this type, there is no way to move a neural nerve backwards and train the previous layer. The basic application of this type of network is found in face recognition and related projects, people who are interested in the applications such as speech recognition prefer to choose this type of network to avoid the complexity.
This layer includes the radial function. The working of this function will be clearer when you know about the structure of this layer. Usually, this network has two layers:
Hidden Layer
Output Layer
The radial function is present in the hidden layer. The function is proved helpful in reasonable interpolation during the process in which the data is fitted into the layers. The layer works by measuring the distance of the nerve from the distance of the central part of the network. For the best implementation, this network checks for all types of data points and groups similar data points. In this way, this type of network can be used to make the systems such as power restoration.
As you can guess from the name of this network, it has the ability to recur. It is my favourite type of neural network because it learns from the previous layer, and the data is used to predict the output in a precise way. This is one of the main layers, and its work has been discussed many times in this tutorial.
Contrary to the first type of neural network that we have discussed before, the information can be recurred or moved to the previous layer. Here are some important points about this layer:
The first layer is a simple feed-forward layer. In other words, it can not move to the previous layer.
Each layer transmits the data to the next layer unidirectional in the first phase.
If during the transmission of data, the layer is predicting inaccurate results then it is the responsibility of the network to learn by repeating the saving of data.
The main building block of this network is the process of saving the data into the memory and then working automatically to get accurate results.
It is widely used in the text to speech conversations.
Now coming towards an important type of neural network that has a scope worldwide and engineers are working day and night in this field because of the interesting and beneficial applications of this network. Before going deep into the definition of this network, I must clarify what exactly a convolution is. It is the process of filtering the results in a way that can be used to enable activation. The filtering mechanism is repeating and therefore, it yields the perfect results all the time. Usually, it is used in image processing, natural language processing, and similar tasks because it breaks the image into parts and then represents the results according to the choice of the user. It is one of the classical techniques that are used for different purposes when people are working on images, videos, or other related projects. For example, if you want to find the edges or details of the images to replace or edit them in a better way, then this technique will be helpful all the time because, through it, you can play with the images and the components of the images as we are using the pixels for our purpose. If these things seem difficult or complex to you at the moment, do not worry, because all the things will be cleared with the passage of time.
Modularity is considered the basic building block of the neural network. It is the process of dividing complex tasks into different modules or parts and solving them individually so that in the end, the results may be combined together and, finally, we get the accurate ending. It is a faster way of working. You can understand well by considering the example of the human brain, which is divided into the left and right sides and, therefore, can work simultaneously. There are different tasks that are assigned to each part and they work best in their duties.
Random input vectors are fed into a discrete map of neurons. Dimensions and planes are other names for vectors. Its applications include recognizing patterns in data, such as in medical analysis.
Here, I am now discussing the type of network that has more than one hidden layer. It is a little bit complex, but if you have an idea of the cases discussed before, you will easily understand this one. The purpose of using this network is to provide the type of data that is not linearly separable. There are several functions that can be used while working on this network. The interesting thing about this network is, it consists of a non-linear activation function for work.
Here n is the number of the last layer, which can be from 0 to any number according to the complexity of the network. A more useful network contains more layers and in return, is more useful usually.
At the moment, I want to discuss an example of this network because it has a slightly different type of work, and I hope that with the help of this example, you will get the concept of what I am trying to teach you, Consider the case where we want to talk to the personal assistance in our divide and on the practical implementation, it is a simple task of few seconds yet at the backend, there is a long procedure that is being followed so that you may get the required results. Here is a simple sentence that is to be asked of the personal assistant.
The first step of the network is to divide the whole sentence into words so that these can be scanned easily.
We all know that each word has a specific pattern of sound, and therefore, the word is then sampled into the discrete sound waves. Let me revise that "discrete sound signals are the ones that consists of discontinuous points. We get the results in the following form.
Now, it is the time when the system further divides the single word into a single alphabet. As you can see in the image given above, each alphabet has a specific amplitude. In this way, the values of different alphabets are obtained and this data is then stored in the array.
In the next step, the whole data obtained is then fed into the input layer of the network and here the working of recurrent neural network stars. By passing through the input layer, each weight of the alphabet is assigned to the interconnection between the input layer and the hidden layer of the network. At this moment, we need a transfer function that is calculated with the help of the following formula:
In the hidden layers, the weights get assigned to the hidden layers. This process continues for all types of layers, and as we know, the output of the first layer is used as the input by the second layer, and this process continues until the last layer. But keep in mind, this process is only for the hidden layers.
While using speech recognition with the help of the neural network, we use different types of terms, and some of them are :
Acoustic model
Lexicon
By the same token, there are different types of exits. I am not going to explain these terms right now because it is unnecessary to discuss them at the moment.
In the end, we are reaching the conclusion that neural networks are amazing to learn and interesting to understand while working with deep learning. You will get all the necessary information about these networks in this course. We started with the basic introduction of the neural network and saw the structure of the network in detail. Moreover, we found the types of neural networks in detail and all the basic networks are discussed here so that you may compare well why we are using these networks and what type of network will be best for you for learning and training. We suggest feed-forward neural networks for basic use, and you will see the reason behind this suggestion in our coming lecture. Till then, you have to search for other networks; if you find any, discuss them with us. In the next lecture, you will learn about deep learning and neural networks, so stay tuned with us.
Hello students, welcome to the second tutorial on deep learning in the first one, we have learned the simplest but basic introduction of deep learning to have a solid base about what we are actually going to do with deep learning. In the present lecture, we will take this to the advanced level and will learn the introduction with the intention of learning more and more about the introduction and understanding what we want to learn and how will we implement the concepts easily. So, here is a quick glance at the concepts that will be cleared today:
What do we mean by Deep learning?
What is the structure of calculation in neural networks?
How can you examine the Neural Networks?
What are some platforms of deep learning?
Why did we choose TensorFlow?
How can you work with TensorFlow?
As we have said earlier, artificial intelligence is the field that works to take the tasks and work of the human being from the computer that is, the computer act like the human. Computers are expected to think. It is a revolutionary branch of science that deals with the feeding of the intelligence of a human being in the computer for the welfare of mankind and with the passage of time, it is proving itself successful in the real world. With the advancement and enhancement of the complexity of artificial intelligence, the field is divided into different branches therefore, AI has a branch named machine learning and then it is subdivided into deep learning. The main focus of this course is deep learning therefore, we describe it in detail.
All this discussion was to tell you about the basics and the important introduction of deep learning and if it is still not clear to you then do not worry because by getting the information about it throughout the series you will start practising, things will be cleared here.
We have seen the discussion about the neural network before but it was just related to the concept of the weights in the neural network. In the present tutorial, you are going to see another concept about the neural network and the proper working on these networks will be started in the coming sessions.
The neural network is just like the multiple layers of the human brain that contain the input layer where the data is fed in different ways according to the requirement of the network. Moreover, the multiple layers are responsible for the proper training process again and again in such a way that every second layer is more mature and accurate than the first one and in this way, the last one has the most accurate data among the others and this is then fed into the output layer where we can get the results. All these processes occur in a sequence while we are working on the neural network and it is listed below:
In the first step, the product is calculated by keeping the weight of each channel and the value of the input in mind.
The sum of all the products obtained is then calculated and this is called the weighted sum of the layers.
In the next step, the bias of added to the resultant calculation according to the estimation of the neural network.
In the final step, the sum is then subjected to the particular function that is named the activation function.
As we have mentioned the steps, we know that it is not clear that much now in your mind therefore, we are discussing an example of this. By keeping all the steps in mind, we are now working on the practical application of working on a neural network.
We are considering the example in which the 28*28 pixel of an image is observed for its shape. Each pixel is considered as the input for the neurons of the first layer.
The first step is then calculated by using the formula given below:
x1*w1 + x2*w2 + b1
We have taken the simple text example but added the process of each layer with the product of corresponding weight occurs till you reach the last layer. The next step here is calculated as:
Φ(x1* w1 + x2*w2 + b1)
Here the Φ sign indicates the presence of the activation function as mentioned above in the steps. Now, these steps are performed again and again according to the complexity of the task and training until all the inner layers are calculated well and the results are reached by the output layer and we get the results. An interesting thing here is the presence of a single neuron in the last layer that contain the result of the calculation to be shown as the output. The detail of how the neural network work will be discussed in the next tutorials. For now, just understand the outputs.
It seems that you are now ready to move forward. Till now, you were learning what is deep learning and why it is useful but now, you are going to learn how can you use deep learning for different tasks. If you are a programmer you must know that there are different platforms that provide the platform for the compilation ad working of the programming language and these are specific to the limited programming languages.
For deep learning, there are certain platforms that are used worldwide and the most important one will be disused here:
TensorFlow is one of the most powerful platforms specially designed for machine learning and deep learning and it is a free source software library. Although it is a multi-purpose platform it has special features that are used to train machine learning and deep learning projects. You can have an idea of its popularity by the fact that it is presented by the google brain team and it contains the perfect functionality.
The full form of DL4J is Deep Learning For You and as you can guess, it is specialized for deep learning and is written in java for the java virtual machine. Many people prefer this library because of its lightweight and specific design for deep learning.
If you are wondering if we are talking about a device then it is not true. Torch is an open-source library for deep learning and it provides the algorithm for the working of deep learning projects.
It is the API that is written in the TensorFlow deep learning platform. It is used for the best practice and experience for deep learning experts. The purpose of using this API is to have a clean, easy, and more reusable result of the code in less time. You will see this with the help of examples in the next sessions.
As you can guess, we have chosen TensorFlow for our tutorial and lectures because of some important reasons that we’ll share with you. For these classes, I have tested a lot of software that was specially designed for deep learning as I have mentioned some of them. Yet, I found TensorFlow most suitable for our task and therefore, I want to tell you the core reasons behind this choice.
You will see that the training and other phases depend upon different models this is super easy to do with the help of TensorFlow. The main reason is, it provides multi-level models so that the one that suits you best will be present for you all the time according to the complexity and working of your project. As we have mentioned earlier, the Keras API is used with TensorFlow therefore, the high-level performance of both of them results in marvelous projects.
In machine learning and related branches such as deep learning, production is made easiest with the help of the fantastic performance of TensorFlow. It always provides the perfect path towards production and results. It also allows us to have the independence of using different languages and platforms and therefore, it attracts a large audience towards itself.
What is more important in research than perfect experimentation? Tensorflow is always here for the multiple types of experimentation and research options so that you may test your project in different ways and get the best results through a single software. The advantage of the presence of multiple APIs and the availability of handling several languages makes it best for experimentation.
Another advantage of choosing it for the tutorial is, it supports powerful add-on libraries and interesting models, therefore, it will become easy for us to experiment more and explain the results in a different way to approach all types of students.
These are some highlighted points that attracted us towards this software but overall, it has a lot in it and you will understand the points when you will see all of them in action in this series, We will be working totally on the TensorFlow and will discuss each and every step in detail without skipping any single step. The practical performance of each step will lead you to move forward with more interest and to understand each concept, we will use different examples. Yet, I have an idea that more explanation makes the discussion confusing so there will be a balance in the explanation.
As we have described before, TensorFlow is introduced by the google brain team and it was closely collaborating with the machine learning research organization.
TensorFlow is the software library that works in the collaboration with some other libraries for the best implementations of deep learning projects and you will see its work and projects in detail soon when we will move forward in this series. There are different libraries that are important to attach with the TensorFlow when we try to make it ready for the working of deep learning. Some of them are listed below:
Python package Index
Django
Scipy
Numpy
Following are the steps that are used to work on the TensorFlow. Yet, keep in mind, these steps vary according to the need of the time and type of the project.
Import the libraries in TensorFlow.
Assign paths of data sets. It is important to provide the path to column variables as well.
Create the test and train data and for this, use the Pandas library.
In the next step, the shape of the test and train data is printed.
For the training data sheet, the data type is printed for each column.
Set the label column values of the data.
You have to cunt the total number of unique values related to the datasheets.
Add features for the different types of variables.
Built the relationship for the features with a bucket.
Add features for the proper definition of the features.
Train and evaluate the model.
Predict the model and set the output to the test set.
Do not worry if these steps are new to you or if they are confusing for you at the moment, you will see the detail of them in the coming future. Moreover, Some of these steps may be different for different people because coding is a vast area and therefore, it has multiple ways to work in a different environments. So, today we learnt several concepts through this single lecture. We have revised and added some other information in the introduction of deep learning, We also have a discussion about neural networks and saw it working. Moreover, the platforms of deep learning were discussed here out of which, we chose the tensor flow and the reason for this choice was also explained well with the help of different points. In the end, we saw the brief procedure to train and predict the project and you will see all these concepts in action in the coming lectures so stay tuned with us.
Hello friends, I hope you all are having fun. Today, we are bringing you one of the most advanced and trending courses named "Deep Learning". Today, I am sharing the first tutorial, so we will discuss the basic Introduction to Deep Learning, and in my upcoming lectures, we will explore complex concepts related to it. Deep Learning has an extensive range of applications and trends and is normally used in advanced research. So, no matter which field you belong to, you can easily understand all the details with simple reading and practicing. So without any further delay, let me show you the topics that we are going to cover today:
So, let's get started:
Deep Learning is considered a branch of Machine Learning which itself comes under Artificial Intelligence. So, let's have a look at these two cornerstone concepts in the computing world:
Artificial intelligence or AI is the science/engineering behind the creation of intelligent machines, particularly intelligent computer programs. It enables computers to understand human intelligence and behave like it. AI has a broader expertise and does not have to limit itself to biologically observable methods as in deep learning.
It is a field that combines the computer and the robust data set to solve the problems of life. Moreover, it is important here to mention the definition of machine learning:
Machine learning is the branch of artificial intelligence, it learns from the experience and data fed into it and works intelligently on its own without the instruction of the human being. For instance, the news feed that arises on Facebook is directed by machine learning on the data so the content of the user’s choice appears every time when they scroll Facebook. As you put more and more data into the machine, it will learn in a better way to provide intelligent results.
Deep learning uses neural network techniques to analyze the data. The best way to describe the neural network is to relate it to the cells of the brain. A neural network is the layers of nodes much like the network in our brain and all these nodes are connected to each other either directly or indirectly. Neural Network has multiple layers to refine the output and it gets deeper as the number of layers increases.
In the human brain, each neuron is able to receive hundreds or thousands of signals from the other neurons and selects signals based on priority. Similarly, in deep learning networks, the signals travel from node to node according to the weight assigned to them. In this way, the neurons with the heavyweight have more effect on the adjacent layer. This process flows through all the layers and the final layer compiles the weight of the resultant and produces the output.
The human brain learns from its experience i.e. as you get old, you get wiser. Similarly, deep learning has the ability to learn from its mistakes and keeps on evolving.
The process of network formation and its working is so complex that it requires powerful machines and computers to perform complex mathematical operations and calculations. Even if you have a powerful tool and computer, it takes weeks to train the neurons.
Another thing that is important to mention here is that neural network works on binary numbers only. So, when the data is being processed, it classifies the answers as a series of binary numbers and performs highly complex calculations. Face recognition is the best example in this regard because in this process, the machine examines the edges and lines of the face to be recognized and it also saves the information of more significant facial parts.
Understanding the layers in deep learning is important to get an idea of the complex structure of deep learning neural networks. The neurons in the deep learning architecture are not scattered but are arranged in a civilized format in different layers. These layers are broadly classified into three groups:
The working of each neural network in deep learning depends on the arrangement and structure of these layers. Here is a general overview of each layer:
This is the first layer of the neural network and takes the information in the form of raw data. The data may be in the form of text, values, images, or other formats and may be arranged in large datasets. This layer takes the data and applies processing to make it ready for the hidden layers. Every neural network can have one or many input layers.
The main processing of data occurs in the hidden layers of the neural networks. These are crucial layers because they provide the processing necessary to learn the complex relationship between the input feature layer and the required output layer.
There are a great number of neurons in the hidden layers, and the number of hidden layers varies according to the complexity of the task and the type of neural network. These layers perform operations such as improved accuracy, feature extraction, representation learning, etc.
These are the final layers responsible for the production of the network’s predictions. A neural network may have one or more output layers, and the activation function of the network depends on the type of problem to be solved in the network. One such example is the softmax activation function, which divides the output according to the probability distribution over different classes.
To understand well, usually, the example of object or person recognition is explained to the students. Let's say, we want to recognize or detect a cat in the picture. We know that different races of cats do not look alike. Some of them are fluffy, some are short, and some are thin in appearance. By the same token, the different angles of the images of the same cat will not be the same and the computer may be confused to recognize these cases. Therefore, the training process includes the amount of light and the shadow of the object in the observation.
In order to train a deep-learning machine to recognize a cat, the following main procedures are included:
In the modern computing world, deep learning has a wide range of applications in almost every field. We have mentioned a few examples in our above discussion i.e. Facebook newsfeed and driverless cars. Let's have a look at a few other services utilizing deep learning techniques:
Digital assistants i.e. voice recognition, facial recognition, text-to-speech conversion, voice-to-text conversion, language translation, plagiarism checker etc. are using deep learning techniques to recognize the voice or to process languages. Grammarly, Copyscape, Ahrefs etc. are a few real-life examples using Deep Learning techniques.
Paypal is using deep learning to prevent fraud and illegal transactions. This is one of the most common examples of the banking facility that I am mentioning here otherwise, there are different applications in security and privacy that are connected to deep learning.
Some object recognition applications such as CamFind allow the user to use pictures of the objects and with the help of mobile vision technology, these apps can easily understand what type of objects have been captured.
Another major application of deep learning is the self-driven car that will not only be able to minus the need for drivers in the car but are also able to avoid traffic jams and road accidents. It is an important topic and most companies are working day and night in deep learning to get the perfect results.
As we have said earlier, deep learning is the process of training the computer like humans, therefore, people are working best to train the machine so they can easily examine trends and predict future outcomes such as stock marketing and weather prediction. Isn't it helpful that your computer or the assistant tells you about the stock marketing rates and predicts the best option to be picked for your investments?
In the medical field, where doctors and experts are working hard to save the lives of people, there is no need to explain the importance of technologies such as deep learning that can predict and control the values in body changes and suggest the best remedy and solution of the problem being observed.
Once you have read about the application and the working process of deep learning, you must be thinking if it is the future, why choose deep learning as your career? Let me tell you, if you excel in deep learning, the future is yours. The careers in deep learning are not yet declared but in the coming few years, you are going to see tremendous exposure to deep learning and related subjects and if you are an expert in it, you will be in demand all the time because it is coming with the endless opportunities. Machine learning engineers are in high demand because neither data scientists nor software engineers possess the necessary skills.
To fill the void, the role of machine learning engineer has evolved. According to experts, the deep learning developer will be one of the most highly paid ones in the future. I hope, in the future, almost all fields will require the involvement of deep learning in their network to work easily and to get more and more efficient work without the involvement of human beings. In simple words, with the help of a neural network, we are replacing human beings with machines and these machines will be more accurate.
So in this way, we have introduced you to the amazing and interesting sub-branch of machine learning that is connected to artificial intelligence. We have seen the working and procedures of deep learning and to understand well, we have seen the examples of deep learning processes. Moreover, the trends and techniques discussed related to deep learning where we have seen that most popular apps and websites are using deep learning to make their platforms more user-friendly and exciting. In the end, we saw the careers and professions of deep learning for the motivation of the students. I know at this step, you will have many questions in your mind but do not worry because I am going to explain everything without skipping a single concept and will learn new things with you while explaining to you. So stay with us for more interesting lectures.
Welcome to the next lesson of our python programming class. In the last session, we looked at the use of If-else statements and created simple programs to demonstrate the concept. While loops are another type of conditional statement, and in this tutorial, we'll look at how they're used.
In this article, you'll learn how to prematurely exit a while loop, a Python control structure for endless iteration. You will also learn how to write conditions in a single line to produce short and readable code.
The term "iteration" refers to the practice of repeatedly running the same piece of code. A loop is a type of programming structure that implements iteration.
Both indefinite and definite iterations are recognized as valid programming constructs. Infinite iteration doesn't specify how many times the loop will be executed. As long as a condition is met, the chosen block will be executed again. An iteration that uses a definite iteration specifies how many times it will run the selected block.
While Loops are used to iterate over the same code block for an unlimited couple of iterations until a condition is met.
While Loops are used to repeat the same code block indefinitely until a condition is met, if a given Boolean condition is completed at the end of the block, the "do while" loop will continue to run a provided block of code, or it will not.
Only one statement is required for the Do While Loop to terminate. However, the While loop may terminate with no statements executed, while the While loop may terminate with statements executed.
This type of control flow statement specifies iteration, allowing the code to be executed again and again. When we know how many times we want to run a code section, a for loop comes in handy.
Here, we'll explore how the Python while statement can be used to build loops. We'll begin with a simple design and gradually add to it.
Listed below is a basic while loop's structure:
Block denotes an execution block that will be repeated over and over again. As with an if statement, this is indicated by indentation.
Indentation is used to define blocks in all Python control structures. See the previous tutorial on grouping statements for a refresher.
In the controlling expression, expr, variables are initialized and updated during the loop's execution.
In this case, the expr parameter is evaluated first in a Boolean context. If this is true, then the loop's body is run. The body is executed when the expression returns to true. The program moves to the topmost statement following the loop body if the condition in expr is false.
Take a look at the following loop:
This is what we're seeing in this case. n starts as five. The loop body is run because n > 0 is true. n is greater than 0. Line 3 of the loop body prints the value of n after decreasing by 1 to 4 places.
The expression is re-evaluated from the top of the loop after the loop's body has completed. Because it's still true, the code in the body runs once again, printing 3 on the screen.
It goes on like this until n equals zero. That is when the expression is tested, and the loop is broken. The program would typically restart execution from the top most statement following the loop's body, but there isn't one here.
It's worth noting that the controlling expression of the while loop is tested first. If the loop body is false at the start, it will never be executed:
Because of this, n is set to zero when the loop is encountered. There is no need for the loop body because the controlling statement n > 0 is false.
Below is another while loop that doesn't use the numeric comparison:
Lists evaluated in a Boolean context are either truthy or false, depending on whether or not they contain elements. As long as there are elements in it, a is true. The loop terminates when the list is empty and all items have been deleted using the pop () method.
The whole body of the while loop is executed each time it is executed. Python has two keywords, break and continue, to prevent a loop from repeating itself.
This statement breaks a loop completely and immediately in Python. Python performs the next statement in the program after the loop body, which ends the current loop iteration. As soon as an expression is evaluated, it is evaluated again to see if the loop will continue or end.
The following diagram illustrates the difference between the statements "break" and "continue":
Break.py is a Python script that demonstrates the use of the 'break' statement:
Using a command-line interpreter to run break.py yields the following results:
The break code is executed when n reaches the value of 2.0. Execution immediately moves to the print () call on line 7 after complete termination of the loop.
A continue statement replaces the break in the next script, continue.py.
The output is something like this:
When n is 2, the loop is terminated by the continue statement. Thus, the number 2 is omitted. The condition is re-evaluated at the top of the loop, and it is still true. As before, the loop terminates when n equals 0.
While loops in Python can have an optional else condition at the end. Unlike most other programming languages, Python has this unique functionality. Syntax in the form of a diagram:
The other clause's further statements will execute when the while loop completes, as shown in this example.
You may be wondering, "How is that useful?" at this point. After the while loop, you could insert the following statements directly after it:
Additional statements will be run regardless of whether or not the while loop is terminated.
An else condition will only execute if the loop stops "by exhaustion"—that is until the controlling condition is false; otherwise, no subsequent statements will be executed. If a break statement is used to break the loop, the else condition is not executed.
Think about the following scenario:
Variable n becomes 0, and therefore n > 0 became false. The loop's block runs until the condition is exhausted. The else clause is executed since the loop is allowed to run its course.
Consider the following example to see the difference:
The else condition is not executed because the loop is prematurely interrupted by the break.
The term else may appear to have a different meaning in the while loop then it does in the if statement. In order to make it more understandable, you may try one of the following:
You are free to disregard either of these interpretations if you don't find them helpful.
If you need to find a certain item on a list, this is a common scenario. If the item is discovered, you can use the break to leave the loop. Also, else condition can contain code to be run if the item is not found:
Assume you've written an infinite while loop. This may sound weird, right?
As an illustration, consider the following:
Ctrl+C, a keyboard interrupt, was used to end this code. There was no way this could have ended. The vertical ellipsis represented in this output has taken the place of several output lines that were omitted.
We're all going to be in a lot of trouble if True can ever be false. On the other hand, False creates an infinite loop that might conceivably run indefinitely.
However, this is a typical pattern, and it may not sound like something you would want to do. Code for a service that accepts service requests and operates indefinitely is a good example. In this situation, "forever" implies until you turn it off or the universe reaches the point of no return.
Alternatively, keep in mind that the break statement can be used to exit a loop. Instead of evaluating a condition at the top, conditions recognized within the loop body may make it easier to terminate the loop.
If you'd rather, you can use pop () to delete each item in the list in turn, as illustrated above:
Not a is true when a is empty, and the break statement terminates the loop.
Multiple break statements can be specified in a loop:
Breaking out of the loop at various points rather than trying to define every possible termination condition in the loop header is often preferable in situations like this.
Infinite loops can be convenient in some circumstances. It's important to remember that the loop must be stopped sometimes, or it will become limitless.
Generally, it is possible to nest Python control structures inside one another. Nested conditional statements, for example, are possible: if/elif/else.
The following code shows how a while loop can be nested inside of another while loop:
Nested loops are affected by statements like "break" or "continue" since they are nested within each other.
Loops can be nested in and out of the if/elif/else statements and the other way around.
All Python control structures can be mixed and matched to your heart's content. That is precisely how it should be. You can only nest while loops a maximum of four deep, which would be unpleasant if there were unforeseen constraints like these: 'A while loop cannot be embedded within an if statement.' Trying to recall them all would be nearly impossible.
Poor programming language design is evidenced by the presence of seemingly random numerical or logical constraints. Python, fortunately, does not have any.
You can specify a while loop in the same way you do an if statement: on a single line. If the loop body has many statements, semicolons (;) can be used to divide them:
To be clear, this technique only works with simple declarative. Compound statements cannot be combined in a single line. As a result, you can write an if statement on the same line as a while loop:
You can also do this:
Note that PEP 8 forbids the use of numerous assertions on a single line. "Since this is a bad idea anyway, you generally shouldn't do it too often.
The Python while loop was used in this tutorial to demonstrate indefinite iteration. You can now create sophisticated and straightforward while loops, thanks to the knowledge you've gained thus far. The else clause can be used in conjunction with a while loop to handle endless loops. If you've followed along, you should understand how to run the same code repeatedly. The next lesson will cover iteration with for loops with an explicit limit on the number of iterations.
Welcome to the fifteenth chapter of this python course. Python lists and tuples were studied extensively in the last session, and we learned how to manipulate the data contained in these types of structures. You've only experienced sequential execution up to this point, where each statement is performed sequentially in the order they appear in the code.
However, the real world is frequently more nuanced. Sometimes, a program must skip over certain statements, run a set of statements repetitively, or pick between other sets of statements to execute. This is called "conditional branching."
That's when control structures come into play, which controls the sequence in which statements in a program are executed.
The if statement is the first control structure you'll encounter in Python.
Real-world situations frequently need us to examine the information around us and then make a decision based on what we've observed. As an illustration;
Unless it's raining, I'll be mowing the yard. It's correct to say that if it's pouring or snowing, I won't be mowing the lawn.
This type of decision-making is performed in Python programs using the if statement. If an expression has a certain value, and that value is known, a statement or set of statements can be executed.
Let`s get started.
We'll begin with the simplest form of an if statement. This is how it appears in its most basic form:
As you can see:
Execution of the statement occurs when the expression evaluates to a "truthy" value. No action takes place if expr is false. You must include a colon (:) after expr. Python does not require the parentheses around expr, as some other programming languages do.
This type of if statement is used in a variety of ways:
There is no effect on pressing Enter key after you have typed the print('yes') expression when using these examples interactively in a REPL session. There are multiple lines in this command. You must press Enter a second time to complete it. Executing a script file doesn't necessitate the use of an extra newline.
Suppose, on the other hand, that you wish to assess a condition and then take many actions if it is true:
There is only one 'statement' in each of the cases above, as demonstrated. It's necessary to be able to express "Do this if [expr] is true."
Syntactic devices, which bring together several statements into a single compound statement or block, are the most common technique employed by most programming languages. Syntactically, a block is considered to be a single entity. Explanation: All statements in the block are performed when it is an "if" target and "expr" is true. None of them are true if expr is false.
It is possible to define blocks in virtually all programming languages, however, this is not always possible. Let's have a look at Python's approach.
You may have heard the offside rule in football, right? Well, in programming, the off-side rule is a tenet of the Python programming language. Indentation is used by rule-abiding languages to define blocks. Off-side rule adherent Python is one of few languages.
Indentation has a specific meaning in a Python program, as you learned in the last tutorial on the structure of Python programs. The reason for this is that indentation is used to denote blocks of related statements. A block in a Python program consists of a series of statements that are all indented the same way. Thus, a Python compound if statement looks like this:
Lines 2 to 5 are considered to be part of the same block because they all have the same indentation level. If expr is true, the entire block executes, while if expr is false, the block is skipped. Following the following statement> (line 6) execution continues.
Tokens are not used to indicate the end of a block. There are two ways to tell when a block has come to a close.
Take foo.py as an example:
This is what happens when you run foo.py:
Lines 2-5 have the same indentation and print () commands. As a result, they form the code that would be executed if the underlying assumption was correct. Because it is untrue, the entire block is ignored. It doesn't matter whether or not lines 2 to 5 are executed, the first statement with a lower indentation level, the print () statement on line 6, is executed.
There is no limit on how deep blocks can be nested. Each new block is defined by a new indent, and each previous block is ended by an outdent. In the end, the structure is simple to follow, consistent, and easy to understand.
This script, called blocks.py, is a bit more complicated.
The following is an example of what you'll see after running this script:
When entering multiline expressions into a REPL session, you must include an extra newline because of the off-side constraint. Otherwise, the translator would have no means of knowing the if block's final statement had been entered.
Perhaps you'd like to know what other options are out there. It's unclear how blocks are declared in languages that don't follow the off-side rule
To denote the beginning and end of a block in most programming languages, special tokens are used as a strategy. Curly braces () are used to define blocks in Perl, for example:
Other programming languages, such as C/C++ and Java, also make use of curly brackets in this fashion.
Algol and Pascal, on the other hand, employ the keywords begin and end to denote the beginning and finish of a block.
It's all about how you look at it. They tend to have a strong opinion about how they do things in general. The off-side rule can generate a lot of controversies when it comes up for discussion.
The off-side rule is an issue you'll have to deal with if you are writing Python code. Python's control structures all rely on it, and you'll see this in several upcoming lectures. Many programmers initially resisted Python's approach to defining blocks, but they've since learned to enjoy it and even prefer it over more traditional methods.
If a certain condition is met, you may wish to conduct a certain course of action, but if it isn't, you may want to specify another course of action. The else clause is used to accomplish this:
If expr> is true, the first suite is run and the second is skipped. Second Suite Execution Is Skipped If 'Expr' Is False Execution resumes after the second suite is completed. Indentation is used to distinguish between the two suites, as indicated in the preceding paragraph. For example, lines 4 to 5 are run, and lines 7 to 8 are omitted because x is less than 50:
Because x exceeds 50 in this case, the first suite is omitted in favor of the second, which is run.
It's also possible to branch execution based on a variety of possible outcomes. One or more elif clauses can be used to do this. Each expr is evaluated in turn by Python, which then executes the set of instructions associated with the first one that is found to be true.
You can provide as many elif clauses as you like. The else clause is not required. One must be provided at the end if it is present:
An if statement with elif clauses, like the ‘and’ and ‘or’ operators, uses short-circuit evaluation. The remaining expressions are not tested when one of the tests returns true and its block is run. This can be seen in the following example:
There is a zero division in the second equation, and an undefined variable var is referred to in the third. As long as the first criterion is met, neither option will be assessed.
The following is a standard way to write if (expr) indented on a separate line from the statement (statement):
However, an entire if statement can be written on a single line. The following is essentially the same as the previous example. Semicolons are used to separate multiple statements on the same line.
One exception to this rule is when an entire if statement is written in one line. Functionally, this is just like the example above. Separated by semicolons, you can have multiple statements on a single line.
Unlike the other if statement forms, this one does not control the flow of program execution, unlike the ones listed above. It's more like an expression-defining operator. Conditional expr> is first evaluated in the above example. The expression evaluates to expr1 if it is true. It returns a value of expr2 if it is false.
It's important to note that the evaluation of the middle expression comes before the evaluation of the two ends, and as a result, only one of the two ends is returned. Here are a few real-world examples to illustrate my point:
Selective assignment of variables is a popular application of the conditional statement. Let's say you're trying to figure out which of two numbers is greater. You could, of course, use the built-in method max() to accomplish the same thing. But what if you want to start from scratch and develop your code?
The term "code stub" refers to a placeholder for a section of code that hasn't yet been implemented, such as when writing a test case.
Token delimiters, such as the curly brackets in C or Perl, can be used to define a code stub in these languages. Perl or C code like the following is acceptable:
The curly braces here denote an empty area. Even if the expression x is true, Perl or C will do nothing after evaluating it.
Specifying an empty block is impossible because Python utilizes indentation rather than delimiters. A follow-up statement, either indented or on the same line, is required after an if statement that begins with if expr. Consider foo.py as an example:
Foo.py doesn't work if it is attempted to be run.
This issue can be solved with the Python pass command. It does not affect the program's behavior. With this placeholder, the interpreter is kept happy in situations where a statement is required syntactically but no action is desired:
Foo.py is now error-free:
Congratulations! You have completed this tutorial on conditional statements in Python. We've explored the if-else statement in Python code and learned how to organize statements into blocks and understand the control structure concept in Python. Developing more complicated Python programs relies heavily on understanding these ideas. The while statement and the for statement are two new control structures that will be introduced in the next tutorial.
Welcome to the fourteenth chapter of our python tutorial course. In the last lesson, we looked at sets and operations done to sets, including union and intersection. In this tutorial, we'll take a closer look at lists and tuples to see how they're used. Python's most versatile and useful data types are lists and tuples. A non-trivial Python application will nearly always have these.
Lists and tuples have a number of significant features that you'll learn about. In this course, you'll understand the definitions and applications of these terms. By the time you're done, you'll know when and how to employ different Python object kinds.
In other words, Lists are similar to arrays in many other programming languages because they allow you to store any number of arbitrary elements within them. For a list to exist in Python, an object sequence must be enclosed in square brackets ([]) as seen in the example below:
In other words, a list is more than a collection of things. Collections of things are organized in this way. Lists are defined by the order in which their elements are listed, and this order is maintained throughout the life of the list itself. For more information on Python data types, check the dictionaries tutorial (coming soon).
A comparison of two lists that contain the same contents but are organized differently is impossible:
A list can be made up of any number of items. A list can have all of its elements of the same type:
Different kinds of elements can be used.
Complex objects such as functions, classes, and modules can also reside in lists, as you'll see in forthcoming tutorials:
From 0 to the limit of your computer's RAM, a list can contain any number of items.
Uniqueness isn't required for list objects. There is no limit to the number of times an object can be listed:
This is a question you could ask yourself whenever you need to access items in a list, and the answer is yes: an index in square brackets can be used to access items in a list. In other words, it's the same as looking up individual characters in a string. As with strings, the indexing of lists is zero-based. The following is a sample list:
Here are the indices for the items in a:
Slicing is another option. For lists, the formula a[m:n] retrieves only the part of a that is between m and but not containing n in the list a.
As you learned before, an item in a list can be of any type. Another list is included in that. You can have as many sublists as you want within a single list.
As an illustration, consider the following (obviously fabricated) scenario:
x refers to an item structure depicted in the image below:
These three strings below, are all one character in length:
Example of sublists are shown below:
Simply add an additional index to have access to the items in a sublist:
To the degree that your computer's RAM allows, there is no limit to the depth or complexity of nested lists in this manner.
A lot of your experience so far has been with atomic data types. Primitive units, such as integers and floats, are those that cannot be decomposed further. Once they've been allocated, these types aren't able to be modified. Changing the value of an integer doesn't make sense at all. If you prefer a different integer, simply change the one you've assigned.
The string type, on the other hand, is a complex type. Strings can be broken down into their constituent characters. Think of a string of characters and how they might be rearranged. However, this is not possible. Strings are also immutable in Python.
This is the first time you've met a mutable data type, the list. It is possible to add or remove items from a list at any time after it has been created. Lists can be modified in a variety of ways in Python.
A single value can be replaced in a list using indexing and simple assignment.
A string can't be used to accomplish this, as demonstrated in the Python tutorial Strings and Character Data.
In order to remove a list item, use the del command:
Suppose you'd like to change several neighboring items in a list at the same time. The following Python syntax for a slice assignment makes this possible.
Consider an iterable list at this point. iterable is substituted for the slice of a specified here:
It's not necessary to have the same number of new elements as the number of old ones. Python simply increases or decreases the list based on the task at hand. Utilize a slice which only refers to one element when you wish to replace a single element with multiple ones:
You can also add items to a list without having to remove anything from the original list. Simply type [n:n] to produce a zero-length slice at the requested index.
You can remove a large number of items from a list by assigning the correct slice to an empty list. It is possible to use the del statement with the same slice:
To add more items to the beginning or end of a list, you can use the + concatenation operator or the += augmented assignment operator:
For example, a singleton list can only have one item in it, hence, it must be added to a different list:
Python provides a number of built-in methods for modifying lists. Below, you'll find more information on these methods. The target string was not directly modified in the previous tutorial's string methods. Strings are immutable, so this is why. String methods, on the other hand, give you back a completely rewritten string object. They don't change the target string at all:
List methods differ from other approaches. Lists are changeable, therefore the target list gets modified while the list method is running.
Adds a new item to the end of a collection.
List functions change the target list on the fly. They don't give you a new one:
Adds items from an iterable to a list.
Yes, it's most likely what you're expecting. Additionally, an iterable is required as an argument to extend(). iterable> elements are inserted one at a time:
To put it another way, extend() functions similarly to the plus sign (+). Because it alters the list while it's still in place, it's equivalent to the += operator:
A new element is added to a collection with the help of this method. Object obj> is inserted into the list an at the index indicated by insert(index>, obj>). It's a[index>] obj, and the remaining list items are moved rightward after the function call.
In a list, this function removes one item. remove(<obj>) list an is cleared of obj. An exception is thrown if obj> is not in a:
In a list, this function removes one item. There are two key differences between this method and remove():
The last item in the list is simply removed by calling pop():
Specifying an index in the optional index parameter causes this command to remove and return the given item. Like string and list indexing, index can be negative.
Python lists are described in this course by a set of six qualities. Finally, lists can be reordered. Sections above have shown many instances of this. A list expands as new things are added:
Similarly, as things are removed from a list, the list gets smaller.
A tuple is a collection of things that are arranged in a specific order. When it comes to the pronunciation of a word or phrase, it depends on who you ask. A few people say it as if it were spelled "too-ple," while others pronounce it as "tup-ple," which rhymes with "supple." Because everyone I know pronounces "supple," "quintuple," "sextuple," and "octuple" as though they rhyme with "supple," my preference is for the latter.
Lists and tuples are nearly identical, with the exception of the following characteristics:
As an illustration of tuples in action, consider the following code sample:
There's no need to worry! Reversing a tuple is as simple as using our usual string and list reversal process:
It's important to remember that although though tuples are constructed using parenthesis, you still use square brackets to index and slice them.
A tuple is a list with the same properties as a list: it's ordered, it can include arbitrary objects; it's indexable and sliceable; and it can be nestable like any other list. However, they cannot be changed:
There is a way to display the values of several objects at once in a Python REPL session by simply inserting them one after the other between commas:
Because Python interprets the input as a tuple, it presents the response in parentheses. The definition of a tuple has a peculiarity that you should know about. It's impossible to be vague when creating a tuple that has no items or a tuple with two or more. A tuple is defined in Python:
Because parentheses are used to denote operator precedence in expressions, the expression (2) creates an int object. Before closing parentheses, you need to put in an extra comma (,): This tells Python that you plan to create a single tuple.
There has to be a mechanism to define a singleton tuple, even if you don't need to do it very often.
Using Python, you can display a singleton tuple by putting a comma in front of it:
You've seen this before: a literal tuple can be allocated to a single object.
When this happens, it's as if the tuple's contents have been "stuffed" into the object:
"packed" objects can be "unpacked" into a new tuple by assigning them to the new tuple's objects.
Otherwise, a problem will emerge when unpacking a tuple: if there are more variables than values, an error will occur.
Compound assignments can be created by combining the steps of packing and unpacking into a single expression.
It's important to remember that in this tuple, the components on the left and right must be equal.
It is possible to leave off the parentheses required to denote a tuple in Python assignments like this one and a few others:
If you're unsure whether or not the parentheses are necessary, go ahead and put them in if you have any doubt. Python idioms are made possible by multiple assignment. As a programmer, it is common to have two variables whose values must be swapped. While the swap is taking place, a temporary variable must be used to store one of the values.
In Python, a simple tuple assignment is all that is needed to perform a swap:
If you have ever used a Python temporary variable to exchange values, this is the pinnacle of modern technology. It's the greatest it's ever going to be.
Congratulations! You have now completed the list and tuple tutorial. Python lists and tuples were introduced, along with some of their basic features and operations. In Python, you'll be relying on these all the time. It is a list's primary property that it is organized. It is impossible to modify the order of a list's elements, unless, of course, the list is altered. The same is true for tuples, except that they can't be updated. Python's conditional statements will be covered in the upcoming lesson.
This is the next lesson in our Python course. Previously, we looked at an overview of the different data types in python such as dictionaries, Boolean and sets. This tutorial will focus on Python sets to get a deeper understanding of this data type, so let's get started. During your schooling, there is a good chance you learned about sets and set theory. Venn diagrams may even be familiar to you:
Don't worry if you don't recognize this! You should still be able to access this tutorial without any problems. Rigidly defining a set in mathematics can be both abstract and difficult to understand. A set is thought of as a well-defined group of unique objects, which are sometimes called "elements."
Python's built-in set type facilitates the grouping of items into sets, which is important in programming as well. Unique actions that can be done on a set separate it from other object types.
Using Python, you learn how to create set objects and learn about the various activities they can be used for. We've covered lists and dictionaries in previous tutorials, so you should be familiar with when a set is the right tool for the job. You'll also look at "frozen sets," which are similar to sets but differ in one significant way.
The following features describe the built-in set type in Python:
Let us explore what all that entails, and how you can interact with sets in Python.
Iter> is an iterable (imagine a list or tuple for now) that generates a list of items to be included in the set. This is the same as the list method's iter> argument .extend():
A string can also be supplied to set() because strings are iterable. As you can see, list(s) generates a list of the characters in the string s. In the same way, set(s) generates a set of the characters in s:
The resulting sets are not in any order. The definition's original order isn't always followed. Values that are duplicated such as the string 'foo' in the first two examples and the letter 'u' in the third are only represented in the set once.
Curly braces () can also be used to define a set:
Each obj> becomes a separate element of the set when defined in this way, even if it is iterable. The .append() list technique works similarly. As a result, the sets depicted above can alternatively be described as follows:
To summarize:
Consider the following differences between these two definitions:
A set can be empty. The set() method is the sole way to define an empty set in Python because empty curly braces () are regarded as an empty dictionary.
In Boolean logic, an empty set is false:
A Boolean variable can only have two values in general: True or False. In other words, we call a variable a Boolean variable if it can only have these two values. It's frequently used to denote an expression's Truth value. True equals 1 and False equals 0 in mathematics. In contrast to electronics, a light bulb has a high value (that is 1) when it is switched on, and vice versa.
The len() function, which returns the number of items in a set, can be used to test for membership with the in and not in operators:
Sets are incompatible with many of the operations that operate with other composite python data types. Sets, for instance, cannot be indexed or sliced. Python, on the other hand, provides set object methods that are quite similar to the operations given for mathematical sets.
Most, but not all, set operations in Python can be accomplished using either an operator or a method. Let's look at how set union works as an illustration of how these operators and methods function. With sets, x1, and x2, the union of the two sets yields a set that contains all members from both sets.
Consider the following:
The results of combining x1 and x2 are shown below.
Note that in the union, 'baz,' will appear in both x1 and x2 only once. There are never any duplicate values in a set.
The | operator in Python can be used to execute set union:
The union() method can also be used to get a set union. The method is called using one of the sets as an input, and the other is supplied as a parameter:
The operator and method operate identically when used in the instances above. However, there is a distinction between them. Both operands must be set when using the | operator. In contrast, the union() method takes any iterable as an input, turns it into a set, and then executes the union.
Take note of the differences between the following two statements:
Both try to combine ('baz', 'qux', 'quux') with x1. The | operator fails, but the union() method succeeds.
A list of Python set operations is shown below. Some tasks are accomplished by an operator, while others are completed by a method, and still, others are completed by both. When a set is required, procedures normally accept any iterable as an input, whereas operators require actual sets as operands.
x1 | x2 [| x3 ...]
Add two or more sets together to get the unionset.
x1.union(x2) and x1 | x2: returns the sets of all items in either x1 or x2.
With either the operator or the method, you can specify more than two sets:
All elements that appear in any of the defined sets are included in the final set.
x1 & x2 [& x3 ...]
Calculate the point at where two or more sets intersect.
The set of items shared by both x1 and x2 is returned by x1.intersection(x2) and x1 & x2:
The intersection method and operator, like set union, allow you to specify multiple sets.
Only components that appear in all of the provided sets are included in the resulting set.
Calculate the difference between at least two sets.
Two examples of x1.difference are x1.difference(x2) and x1 - x2 (x2). produce a list of all x1 elements that aren't found in x2:
difference(x2) and x1 - x2 return the set that is returned when any elements in x2 are removed or subtracted from x1.
You can specify multiple sets once more:
The procedure is executed from left to right when several sets are supplied. In the foregoing example, the first step is to compute a - b, which yields 1, 2, 3, 300. After that, the set is taken from c, leaving 1, 2, and 3:
Calculate the difference between two symmetric sets.
The sets containing all items in x1 or x2, but not both, are returned by symmetric difference(x2) and x1 x2:
Additionally, the operator ^ enables for more than two sets:
The operation is executed from left to right once multiple sets are supplied, just like with the difference operator.
Surprisingly, although the operator supports multiple sets, the symmetric_difference() function does not:
Sets can be altered, even though their components need to be immutable types. Similar to the operations above, the contents of a set can be altered using a combination of operators and processes.
Each of the aforementioned operators has an augmented assignment form that can be used to change a set. Each person takes a different approach.
x1 |= x2 [| x3 ...]
The union can be used to change the state of a set.
x1 &= x2 [& x3 ...]
Intersection can be used to change a set.
x1 &= x2 and update(x2) x1 should be updated with only the items that present in both x1 and x2:
x1 -= x2 [| x3 ...]
Make a difference in a set.
x1.difference update(x2) and x1 -= x2 remove components found in x2 from x1:
x1 ^= x2
By using symmetric difference, you can change a set.
x1=x2 and update(x2) update x1, maintaining either x1 or x2 components, but not both:
Aside from the augmented operators listed above, Python has several other ways of modifying sets.
Adds a new element to a collection.
x.add(elem>) appends elem> to x:
Removes one of a set's elements.
elem> is removed from x using x.remove(elem>). If elem> is not in x, Python throws an exception:
Removes one of a set's elements.
elem> is also removed by x.discard(elem>). If elem> is not in x, this procedure does nothing instead of issuing an exception:
A set contains the random element to be removed from it.
x.pop() removes and returns an element from x that is picked at random. x.pop() throws an exception if x is null:
A frozenset is a Python in-built type that is similar to a set but is immutable. The following non-modifying procedures are possible on a frozenset:
Attempts to change a frozenset, on the other hand, fail:
You might suppose that because a frozenset is immutable, it can't be the target of an augmented assignment operator. However, keep the following in mind:
With frozensets in place, Python does not perform augmented assignments. The expression y &= s is practically the same as y = y & s. It makes no changes to the original x. It's associating x with a new item, and the one with which it was previously connected has vanished.
The id() method can be used to check this:
Following the augmented assignment, f has a new integer identification. It has been reassigned rather than changed in situ. When a Python object is the target of an augmented assignment operator, it is updated in place. Frozensets, on the other hand, are not. Frozensets are useful in cases where you need an immutable object yet wish to utilize a set. For example, because set elements must be immutable, a set with items that are also set cannot be defined.:
When you need to define a set of sets, frozensets, which are immutable, are the way to go:
Remember from the previous dictionary instruction that a dictionary key must be immutable. The in-built set type can't be used as a dictionary key for the following reason:
If you're looking for a way to use sets as dictionary keys, try frozensets:
We must use a membership operator to see if an element exists in a set. To check if an element is present in a sequence, membership operators are employed (e.g., strings, lists, tuples, sets, or dictionaries). As mentioned below, there are two membership operators.
Use the len () function to calculate the total number of items in a set. The number of items in an object is returned by this function. The function's input can be any sort of sequence, including a text, dictionary, list, or tuple, in addition to a set.
This tutorial teaches you how to create set objects in Python and how to interact with them using functions, operators, and methods. Python's main built-in data types should now be familiar to you. Then you'll examine the organization and structure of the code of a Python program that interacts with those items. In the next topic we will look at python list and python tuple.
