Things All Beginners Should Know About Convolutional Neural Networks

Convolutional Neural Networks is very similar to the interpretation of information processing and communication in the biological nervous system. An example of this is nerve coding. This pattern of communication is present in machine learning. There are different methods and each is based on Convolutional Neural Networks.

Thanks to this approach, much better results are achieved in the fields of computer vision, speech recognition, board games programs, etc. To get a better idea of ​​how information passes through this system, keep reading.

Image classification

This is definitely one of the most interesting features of neural networks. People and computers classify images differently. While it is quite natural for you to recognize images from everyday life and do it subconsciously, it is not as nearly as easy for machines. A large number of if / else statements need to be entered for just one plain code. Although at the moment the whole process seems like super power, computers have to perform a large number of tasks in order to achieve the desired results. Computers can’t tell the difference between photos of tumors and corgi, but people have taught them to use previous experiences to recognize things. This is achieved by using CNNs. In this case, the computer will adopt a huge number of images to absorb the necessary data.

Neural network components

Given the characteristics of these networks, they apply to much of the technology we use today and use to communicate. This applies to all the apps I use, but also to a simple Google search. They are also used in more serious computer tasks such as diagnosing tumors. So, neural networks are an integral part of our lives, although many people wonder how they actually work. In order to explain this to you as well as possible, we need to look at the main components. No, it’s not a robot that thinks the same way as a human or some fancy mathematical equation. This is the kind of learning that covers a lot. It covers simple decision-making, neural networks, and complex algorithms.

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Thus, it mimics brain functions. Therefore, their design is adapted to the internal workings of the biological brain. The network is composed of interconnected neurons that must pass input features through a number of layers. They are called perceptrons and are the smallest components of the neural network. Each perceptions changes the input using a set of functions. The smallest components of the neural network consist of scalar multiplication, and summation and activation function. These three operations mimic a neuron in the biological brain and form a convolution neural network.

The task of prediction

For an even better understanding, try to do one simple task. This is an illustrative example, predicting the height of a tree based on the soil content in the ground. We will probably think about the health of the tree, its type, distribution and other characteristics. We will also consider the soil content. We will create a set of data based on when we will form an opinion. The computer will do the same, but there is a big difference between a machine and people.

While we will approximate the height of the tree, the computer will mimic the functions of the brain, but will use the exact equations that determine the prediction. People will speculate a lot, and a hypothetical relationship is not a good enough estimate. There are many factors to consider and it is the convolution neural networks that are responsible for understanding them. It shortens long-term processes, because a model was created that is composed of much simpler equations. They are tested to determine how well they work. After the appropriate equation is set, knowledge transfer follows and the problem is solved.

Processes

Now that the structure of this network is a little clearer to you, we can proceed with the explanations. Thus, the model consists of two main sectors. On the one hand we have learning characteristics while on the other hand there is classification. Take the first sector as an example. Mathematical operations take place here that will extract all the characteristics from the images. The process includes convolutions and pooling. These are simpler functions, such as recognizing the edges of simpler shapes. Inputs are used for information classification purposes. However, in order for the image to be prepared for entry into CNN, it requires pre-processing. This refers to changing the dimension of the pixels that match the model.

When we get the image, convolution occurs, that is, the characteristics of the image are detected. It is scanned as a result of which multiplication by elements with pixel values ​​is performed. Negative values ​​are removed. As the process progresses, we get more and more parameters which means more data and calculations. In that case, different types of pooling are used, average, sum, etc. Feature maps are scanned and the highest value is taken. This is followed by alignment of feature maps, conversion and obtaining fully connected layers, a regular neural network.

How do these things work?

Training is the key to the whole process and it is responsible for the functioning of neural networks. So, we are talking about the most important part. We mentioned earlier that layer filters can look for edges and curves after which negative values ​​are removed. The linked layer recognizes activation maps, filters have insight into each layer and match the value, all as a result of the training process. It’s called backpropagation. It takes place through 4 sections: the forward pass, the loss function, the backward pass, and the weight update.

The learning speed is chosen by the programmer. The difference between the degree of learning is in taking the number of steps in updating the difficulty. Too high a learning rate is not recommended because the consequences are too many jumps and insufficient precision. These 4 sections represent one iteration of the training. The training iteration is repeated and after the parameters are updated, the network will be well trained enough. To make sure that CNN works, testing or forwarding the image through CNN is performed. The obtained results are compared with ground truth.

Conclusion:

Companies around the world use CNN. One of the leading CNN companies is Meta. For the purpose of the company’s needs, photos of users are used. Pinterest also uses pin information, Google uses search data, and so on. Now you know what is behind it.