Technology has moved steadily ahead over the years, but it has evolved by leaps and bounds in the past decade or so. Smartphones have been a revolution and a revelation. Even video games have become increasingly sophisticated and have overtaken the movie industry in value.
If technology keeps developing on this trajectory in the future, the next generation of coders will need online lessons today. Before signing up for your child, here’s what to look for in a program.
For now, put lofty things like your child’s eventual career or the fate of future technology out of your mind. The extracurricular programs kids sign up for need to be fun! Industry leaders like Real Programming 4 Kids make their courses revolve around teaching students to create their own video games.
Kids don’t need to be pushed very hard to play video games, and they are just as drawn to programming them. They can play the games with friends and family after, which is a big motivator.
The best online coding courses also harness gamification dynamics in the sessions, so the same things that make games so addictive and engaging for kids are used for learning.
Credit: Mati Mango via Pexels
Even the best teachers teaching the most engaging subject will struggle if there are too many students packed into a classroom. This is true in online and offline classrooms.
Look for a program that limits class sizes. Four is a great cut-off number, so there is only your child and, at most, three other students. Teachers shouldn’t have to deal with classroom management issues, and students shouldn’t contend against teachers who can’t remember every student’s name because there are so many.
Ideally, the program hires teachers who also grew up playing computer games, as their passion and first-hand experience reach students. Plus, they also have practical experience navigating the job market as a coder, and older students have someone whose brain they can pick about where coding can take them later on.
Learning how to code teaches many useful general computer skills and even fundamental math concepts, like integers, vectors, and trigonometry. Still, kids must also learn the direct skills powering today’s most popular apps, websites, and video games. Employers expect the people they hire to know these languages, and this knowledge lets kids forge their own paths in whatever direction they like.
Here’s a list of the coding languages elite programs teach:
Learning how to write computer code teaches kids how to use lateral thinking like an engineer, problem-solve, and other intangible mental habits. However, nothing replaces knowing the specific languages needed to make programs work.
Society needs to keep up the torrential pace of technological innovation, and parents are looking for fun, beneficial extracurricular activities for their kids today. Even if your child never becomes a professional video game developer or programmer, they’ll be excited to learn and play in a safe, stimulating environment every week. And maybe after they advance in coding, they will develop the next multimillion-dollar video game or generation-defining technology.
Hi learners! I hope you are having a good day. In the previous lecture, we saw Kohonen’s neural network, which is a modern type of neural network. We know that modern neural networks are playing a crucial role in maintaining the workings of multiple industries at a higher level. Today we are talking about another neural network named EfficientNet. It is not only a single neural network but a set of different networks that work alike and have the same principles but have their own specialized workings as well.
EfficentNet is providing groundbreaking innovations in the complex fields of deep learning and computer vision. It makes these fields more accessible and, therefore, enhances their range of practical applications. We will start with the introduction, and then we will share some useful information about the structure of this neural network. So let’s start learning.
EfficientNet is a family of neural networks that are part of CNN's architecture, but it has some of the latest and even better functionalities that help users achieve state-of-the-art efficiency.
The efficientNet was introduced in 2019 in a research paper with the title “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.” Mingxing Tan and Quoc V. Le introduced it, and this is now one of the most popular and latest neural networks. These are Google’s AI researchers, and the popularity of this neural network is due to its robust applications in multiple domains.
The motivation behind EfficentNet's development is the popularity of its parent neural network, CNN, which is an expensive and efficient network. The deployment of CNN in resource-constrained environments such as mobile devices was difficult, which led to the idea of an EfficentNet neural network.
The EffcinetNet has a relatively simpler working model than the CNN to provide efficiency and accuracy. The basic principle of working is the same as in CNN, but EfficeintNet archives better goals because of the scaleable calculations. The convolution of the dataset allows EffcientNet to perform complicated calculations more efficiently. This helps EffcientNet a lot in the processing of images and complex data, and as a result, this neural network is one of the most suitable choices for fields like computer vision and image processing.
As we have mentioned earlier, EffcientNet is not a single neural network but a family. Each neural network has the same architecture but is slightly different because of the different working methods. Some parameters are important to understand before knowing the difference between these members:
When the topic is a neural network, the FLOPs denote the number of floating points per second a neural network can perform. It means the total number of billions of floating point operations an EffcinetNet member can perform.
The parameters define the number of weights and biases that the neural network can learn during the training process. These are usually represented in millions of numbers, and the user must understand that the 5.3 parameter means the particular member can learn 5.3 million parameters it can train.
Accuracy is the most basic and important parameter to check the performance of a neural network. The EffecntNet family varies in accuracy, and users have to choose the best one according to the requirements of the task.
Different family members of EffcientNet are indicated by numbers in the name, and each member has a slightly larger size than the previous one. As a result, accuracy and performance are enhanced. Here is the table that will show you the difference among these:
| Member Name | FLOPs | Parameters | Accuracy |
B0 |
0.6 |
5.3 |
77.1% |
B1 |
1.1 |
7.8 |
79.1% |
B2 |
1.8 |
9.2 |
80.1% |
B3 |
3.2 |
12.0 |
81.6% |
B4 |
5.3 |
19.0 |
82.7% |
B5 |
7.9 |
31.0 |
83.7% |
B6 |
11.8 |
43.0 |
84.4% |
B7 |
19.8 |
66.0 |
84.9% |
This table shows the trade-off between different parameters of EffcientNet models, and it shows that a larger size (increased cost) can be more useful and accurate, and vice versa. These eight members are best for particular types of tasks, and while choosing the best one for the particular task, some other kinds of research are also important.
The workings and structure of every family member of EffcientNet are alike. Therefore, here is a simple and general overview of the features of EffcientNet. This will show the workings and advantages of the EfficientNet neural network.
One of the most significant features of this family is the compound scaling, which is different from other options for neural networks. It has the power to maintain the balance between the following features of the network:
As a result, the EfficientNet network does not require additional computation and provides better performance.
A difference between the traditional CNN and EffientNet neural networks is the depthwise separable convolutions. As a result, the complexity of this network is less than CNN's. All the channels use a separate convolutional kernel; therefore, depthwise separate convolutions are applied to the channels.
The resultant image is then passed through a pointwise convolution. Here, the outputs of the depthwise convolution channel are combined into a single channel. The standard convolution requires a great deal of data, but this technique requires a smaller number of parameters and significantly reduces the complexity.
The EffcientNet family uses a different and more recent type of convolution known as MBConv. It has a better design than the traditional convolution. The depthwise convolutions and pointwise linear convolutions can be done simultaneously. It is useful in reducing floating-point operations for overall performance. The two key features of this architecture are:
Here is a simple introduction to both:
The inverted bottleneck has three main convolutional layers:
This is applied during the computation of the inverted bottleneck. This adds the shortcut connection around the inverted bottleneck, and as a result, the inverted residual blocks are formed. This is important because it helps reduce the loss of information when convolution is applied to the data.
The representational power of EffcientNet can be enhanced by using an architecture called Squeeze and Excite, or SE. It is not a particular or specialized architecture for EfficinetNet but is a separate block that can be incorporated into EfficentNet. The reason to introduce it here is to show that different architectures can be applied to EfficnetNet to enhance efficiency and performance.
The efficeintNet is a family, and therefore, it has multiple sets of workings out of which, the user can choose the most accurate. The eight members of this series (E0 to E7) are ideal for particular tasks; therefore, these provide the options for the user to get the best matching performance. All of these provide a different type of combination of accuracy and size, and therefore, more users are attracted to them.
Hence, this was all about EffientNet, and we have understood all the basic features of this neural network. The EffenctNet is a set of neural networks that are different from each other in accuracy and size, but their workings and structures are similar.
EffcientNet was developed by the Google AI Research team, and the inspiration was CNN. These are considered the lightweight version of the convolutional networks and provide better performance because of the compound scaling and depthwise convolutions. I hope it was helpful for you and if you want to know more about modern neural networks then stay with us because we will talk about these in the coming lectures.
DevOps engineers have a challenging job to do. They are responsible for managing servers, code, and many other components in a software project. They routinely employ numerous tools and calculators to facilitate their daily activities.
Engineers working in DevOps employ a range of calculators to reduce the complexity of any given issue. These tools facilitate and speed up the work. Planning, risk management, and performance optimization can all benefit from them.
Here are some tools that can be useful to a DevOps engineer.
Time is money! Estimating how long a task will take is crucial. This calculator is used by DevOps engineers to estimate various calculations. This tool aids in making time estimates for projects. To predict the amount of time needed in the future, they analyze historical data.
In every field, a budget estimate is required. DevOps engineers use this tool to control the budget because of this. They have a fantastic choice in using this cost calculator to choose an acceptable ratio of cost to keep inside the budget.
Performance is important in all facets of life. CPU load calculators are used to determine the load on a server. They help DevOps engineers decide if more servers are needed. Or maybe they can improve the ones that are already in place. It encourages hasty decision-making.
Data travels swiftly. How rapidly should it progress though? The main purpose of bandwidth calculators is to address this issue. DevOps engineers can use this tool to calculate the amount of bandwidth needed for effective operation. They are necessary to stop system idleness.
Data must be kept in large quantities to prevent loss. These math calculators have been programmed to address this issue. Storage calculators give a precise picture of the amount of space needed. They forecast how much room would be needed over time. The fear of running out of storage is less likely as a result.
To quantify the risks, DevOps engineers employ risk assessment tools. This enables them to combat several unforeseen circumstances that can jeopardize their business. They estimate the likelihood of different dangers.
Return on investment (ROI) is a crucial concept. This calculator is used by DevOps engineers to calculate the benefits of an investment. Like other jobs, measuring the investment or its rewards is crucial. They compare and contrast the costs and benefits. This helps to justify the price of new machinery or systems.
A DevOps engineer is using this tool to calculate the latency in data transport. This helps them identify the problem causing the data transfer delay so that it may be properly repaired. Understanding network delay can help you provide a better user experience.
Hi there! I hope you are having a great day. The success of the field of deep learning is due to its complex and advanced neural networks. These networks can be broadly divided into traditional and modern neural networks. We have seen the details of traditional neural networks, and in the previous session, the basic introduction of modern neural networks and the details of their features were discussed. Today, we will talk about one of the most famous modern neural networks, the Kohonen Self-Organized Neural Network.
Modern neural networks are more organized and developed than traditional neural networks, but that does not make traditional neural networks less efficient than modern ones. All the networks are introduced for specific tasks, and this is one of the main reasons behind the evolution of deep learning in every field. The details of Kohonen's Self-organizing Neural network will prove it, so let’s start learning.
The Kohonen Self-organizing network is also known as the self-organizing feature map (SOFM), and It was developed by Teuvo Kohonen in the 1980s. It is a powerful type of unsupervised learning, the main purpose of which is to map the high dimensional input data even at the lower dimensional grid. It can be used on two or more dimensional data where the neurons are connected and each layer is weighted according to the calculations.
Throughout the data dimensions, the topological properties of the data saved in them remain preserved. During the training process, the self-organizing map learns to organize itself with similar data points and creates a connection with the nearby neurons of the grid.
The training process for SOMs uses competitive learning methods. Think of the scenario where, when new data is added to the network, a quick calculation is made to find the neuron with the same data weight. The most suitable neuron is called the best matching unit (BMU), and adding the new data stimulates it. As a result of this addition, the weights of BMU and their neighbors are updated according to the data. It makes all the neurons similar to each other, and as a result, the network becomes better with time. Here are the details of the key features that we have just discussed:
Topology preservation is the feature of the algorithm that maintains the spatial relationship and the structure of the data that it uses. This all happens when the data is mapped on the lower dimensional grid.
The basic objective of topology preservation is to maintain the structure of the map. This feature preserves the data when it is mapped from higher to lower dimensional space.
This is the basic feature of the Kohonen neural network. The data is arranged in the form of a grid of nodes and neurons. Each of these represents a specific region or cluster of the input data. It becomes easy to maintain the structure of neurons with similar sizes and properties.
This is another way to organize the data in the SOM, and here, the BMU plays a vital role. This feature is responsible for checking two important parameters throughout the processing:
Learning rate
Neighbourhood operation
Here, the learning rate defines the magnitude of the update rate of neurons, and neighborhood operation means the measure of the change in the properties of neighboring neurons when new data is introduced in the model.
Competitive learning helps the network in processes like clustering and visualization. The network autonomously discovers the inherited structure without any need for supervision. It is an iterative process that helps the network grow and learn at a rapid rate.
Understanding the advantages of using Kohonen’s self-organizing network will clarify the significance of this network. Here are some important points about it:
Once you have understood the applications, you are ready to learn about the industrial uses of Kohonen’s self-organizing neural network. The workings of SOM are so organized and automatic that many industries rely on them for the most sensitive calculations, and their results affect the overall performance of that industry. Here are some examples:
The analysis of complex datasets by data mining companies is an important task. Many companies use SOM for such processes where the patterns have to be observed carefully to provide detailed analyses. Different techniques are useful in this regard, but SOM is used here because of the organized pattern and competitive learning.
Some of these companies provide tools for data exploration to their clients. Some provide customer segmentation and anomaly detection. All of these require the use of powerful neural networks, and they use SOM along with other networks for this.
In industries where financial records are imported, this technique detects fraud. For instance, it identifies the patterns of stock marketing and helps detect any abnormal bhavior. In addition to this, processes like risk assessment and credit storage are improved with the help of SOM. This is done in the institutes that are working globally, and a large community has to be handled by the institutes.
The advancement in technology has provided multiple advantages, but it has also led to increased security risks. The SOM is helpful in dealing with such issues. Here are some points to justify how SOM is helpful in different types of technical crimes:
Hence, today we have seen the details of Kohonen’s self-organizing neural network. It is a type of modern neural network that is helping people in different applications in real life. We have seen the features and workings of this neural network, and to understand its importance, we have seen its applications and advantages at different levels. I hope it was helpful to you, and if you want to know more types of modern neural networks, then we will discuss these in the coming sessions. Happy learning.