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?
Introduction to the Python Coding Language
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:
Readable Code
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.
Multiple Programming Paradigms
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.
Flexibility with Other Platforms
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.
Robust Libraries of Python
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.
Opensource Frameworks
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.
Test Driven Approach
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.
Consistency
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.
Massive Community Support
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.
Other Programming Options for The Deep Learning
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.
