First south Asian Edition published in 2019.

Chapter 1: Introduction to Deep Learning; Historical Context; Advances in Related Fields; Prerequisites ; Overview of Subsequent Chapters; Installing the Required Libraries ;
Chapter 2: Machine Learning Fundamentals; Intuition; Binary Classification; Regression; Generalization; Regularization; Summary; Chapter 3: Feed Forward Neural Networks; Unit; Overall Structure of a Neural Network; Expressing the Neural Network in Vector Form; Evaluating the output of the Neural Network. Training the Neural NetworkDeriving Cost Functions using Maximum Likelihood; Binary Cross Entropy; Cross Entropy; Squared Error; Summary of Loss Functions; Types of Units/Activation Functions/Layers; Linear Unit; Sigmoid Unit; Softmax Layer; Rectified Linear Unit (ReLU); Hyperbolic Tangent; Neural Network Hands-on with AutoGrad; Summary;
Chapter 4: Introduction to Theano; What is Theano; Theano Hands-On; Summary;
Chapter 5: Convolutional Neural Networks; Convolution Operation; Pooling Operation; Convolution-Detector-Pooling Building Block; Convolution Variants; Intuition behind CNNs; Summary.
Chapter 6: Recurrent Neural NetworksRNN Basics; Training RNNs; Bidirectional RNNs; Gradient Explosion and Vanishing; Gradient Clipping; Long Short Term Memory; Summary;
Chapter 7: Introduction to Keras; Summary;
Chapter 8: Stochastic Gradient Descent; Optimization Problems; Method of Steepest Descent; Batch, Stochastic (Single and Mini-batch) Descent; Batch; Stochastic Single Example; Stochastic Mini-batch; Batch vs. Stochastic; Challenges with SGD; Local Minima; Saddle Points; Selecting the Learning Rate; Slow Progress in Narrow Valleys; Algorithmic Variations on SGD; Momentum. Nesterov Accelerated Gradient (NAS)Annealing and Learning Rate Schedules; Adagrad; RMSProp; Adadelta; Adam; Resilient Backpropagation; Equilibrated SGD; Tricks and Tips for using SGD; Preprocessing Input Data; Choice of Activation Function; Preprocessing Target Value; Initializing Parameters; Shuffling Data; Batch Normalization; Early Stopping; Gradient Noise; Parallel and Distributed SGD; Hogwild; Downpour; Hands-on SGD with Downhill; Summary;
Chapter 9: Automatic Differentiation; Numerical Differentiation; Symbolic Differentiation; Automatic Differentiation Fundamentals. Forward/Tangent Linear ModeReverse/Cotangent/Adjoint Linear Mode; Implementation of Automatic Differentiation; Source Code Transformation; Operator Overloading; Hands-on Automatic Differentiation with Autograd; Summary;
Chapter 10: Introduction to GPUs;
Chapter 11: Introduction to TensorflowChapter 12: Introduction to PyTorchChapter 13: Regularization TechniquesChapter 14: Training Deep Leaning Models

Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process. Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms. This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included. Deep Learning with Python also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments. You will: Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe Gain the fundamentals of deep learning with mathematical prerequisites Discover the practical considerations of large scale experiments Take deep learning models to production.