Description 
1 online resource 
Contents 
At a Glance; Contents; About the Author; About the Technical Reviewer; Acknowledgments; 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 

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 Minibatch) Descent; Batch; Stochastic Single Example; Stochastic Minibatch; Batch vs. Stochastic; Challenges with SGD; Local Minima; Saddle Points; Selecting the Learning Rate; Slow Progress in Narrow Valleys; Algorithmic Variations on SGD; Momentum 

Forward/Tangent Linear ModeReverse/Cotangent/Adjoint Linear Mode; Implementation of Automatic Differentiation; Source Code Transformation; Operator Overloading; Handson Automatic Differentiation with Autograd; Summary; Chapter 10: Introduction to GPUs; Summary; Index 

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; Handson SGD with Downhill; Summary; Chapter 9: Automatic Differentiation; Numerical Differentiation; Symbolic Differentiation; Automatic Differentiation Fundamentals 

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 Handson with AutoGrad; Summary; Chapter 4: Introduction to Theano; What is Theano; Theano HandsOn; Summary; Chapter 5: Convolutional Neural Networks; Convolution Operation; Pooling Operation; ConvolutionDetectorPooling Building Block; Convolution Variants; Intuition behind CNNs; Summary 
Summary 
Discover the practical aspects of implementing deeplearning solutions using the rich Python ecosystem. This book bridges the gap between the academic stateoftheart and the industry stateofthepractice 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 knowhow 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 
Bibliography 
Includes bibliographical references 
Notes 
Online resource; title from PDF title page (EBSCO, viewed April 20, 2017) 
Subject 
Data mining.


Machine learning.


Python (Computer program language)

Form 
Electronic book

ISBN 
1484227662 (electronic bk.) 

9781484227664 (electronic bk.) 
