Introduction to Neural Networks

Neural networks are a class of machine learning algorithms inspired by the structure and the functioning of the human brain. They consist of interconnected nodes, called neurons, organized into layers. Each neuron receives inputs, processes them using a mathematical function, and produces an output. The connections between neurons are associated with weights, which determine the strength of influence one neuron has on another. 

Neural networks learn by adjusting the weights of connections between neurons based on examples in a process called training. This is typically done using optimization algorithms like gradient descent to minimize a loss function, which measures the difference between the network's predictions and the actual targets. Through this iterative process, neural networks can learn to perform tasks like image recognition, natural language processing, and more, making them powerful tools in artificial intelligence and machine learning. Advanced neural networks are widely used inside most of the Generative AI tools, such as ChatGPT or Dall-E.

A good introduction to neural networks can be found in the following free online book:

Nielsen, M.A. (2015) Neural networks and deep learning. Determination Press. 
Available at: http://neuralnetworksanddeeplearning.com