A Guide to Understanding and Building Artificial Neural Networks in Python

This book covers everything required to start in the field of deep learning by fully understanding, practicing, and building neural networks. Starting from the simplest model Y=X, the book gives intensive full step-by-step math and Python examples to clarify the neural network calculations. The gradient descent algorithm is discussed by examples for training a neural network with any architecture until building a generic Python implementation from scratch mainly using NumPy. So, rather than just using an implemented version of the neural network like that in Scikit-Learn, the reader will both understand and implement it themself. Because building such an implementation is not easy, the book plays a crucial role in simplifying it. This book ensures understanding even if the reader is familiar with neural networks.

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