Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory

Overview & Synopsis

This book aims to provide an introduction to the topic of deep learning algorithms. We review essential components of deep learning algorithms in full mathematical detail including different Artificial Neural Network (ANN) architectures (such as fully-connected feedforward ANNs, convolutional ANNs, recurrent ANNs, residual ANNs, and ANNs with batch normalization) and different optimization algorithms (such as the basic Stochastic Gradient Descent (SGD) method, accelerated methods, and adaptive methods).