What Is Self-Organizing Map (SOM)?

So, do you want to learn about an artificial neural network called a Self-Organizing Map (SOM)? Well, let's get right to it! SOM A map is an artificial neural network that uses unsupervised learning to build a two-dimensional map of a problem space. The critical difference between a map and other problem-solving approaches is that the map uses sociality. Learning rather than error-correction learning, such as backpropagation with gradient descent. Training a SOM is to perform better on some task or optimization criterion. For example, if you are preparing an image recognition system, the task might be correctly classifying images as "cat" or "dog. " To train a SOM for this task, we would provide it with many examples of cat and dog images and allow it to adjust its internal parameters to improve its classification accuracy over time. The human visual cortex is a part of the brain processing visual stimuli. It comprises many regions that can process information about various aspects of our environment, such as color or motion. In this project, we'll explore how we can attempt to model this in a computer system using artificial neural networks. We'll create a network with no input layer and one hidden layer. We'll start with an arbitrary number of nodes in each layer and then train the network using backpropagation until it has learned how to process images correctly. The result will be a network that can recognize specific objects based on their shape and color characteristics. As the neural network moves through the problem set, the weights look more like the data. The neural network has thus trained itself to see patterns in the data much the way a human sees. It's like this: imagine you're trying to figure out what shape your friend's face is. You start with many shapes but then focus on the ones most similar to your friend's face until only one form remains. That's how it works!