Neural Networks and Deep Learning

Neural Networks and Deep Learning is an open-source collection of Python code examples accompanying Michael Nielsen's educational book of the same name. It helps students, researchers, and developers understand how neural networks function through practical implementations. The project demonstrates fundamental concepts such as feedforward networks, stochastic gradient descent, backpropagation, and neural network training. Its examples include recognizing handwritten digits using the MNIST dataset. The repository also explores more advanced neural network architectures through implementations using NumPy and Theano. The code encourages experimentation with network parameters, optimization techniques, and learning algorithms. Originally developed for Python 2, the project serves as an educational reference rather than a modern production framework.