Ganesh Tinniam V. Deep Learning from first principles: In vectorized Python, R and Octave

Amazon Digital Services LLC, 2018. — 770 p.The last decade and some, has witnessed some remarkable advancements in the area of Deep Learning. This area of Artificial intelligence (AI) has proliferated into many branches - Deep Belief Networks, Recurrent Neural Networks, Convolution Neural Networks, Adversorial Networks, Reinforcement Learning, Capsule Networks and the list goes on. These years have also resulted in Deep Learning to move from the research labs and closer to the home, thanks to progress in hardware, strorage and cloud technology.This is the second edition of the book. The code has been formatted with fixed with a fixed width font, and includes line numbering. This book derives and builds a multi-layer, multi-unit Deep Learning from the basics. The first chapter starts with the derivation and implementation of Logistic Regression as a Neural Network. This followed by building a generic L-Layer Deep Learning Network which performs binary classification. This Deep Learning network is then enhanced to handle multi-class classification along with the necessary derivations for the Jacobian of softmax and cross-entropy loss.