Deep Learning with Python - A Hands-on Introduction

von: Nikhil Ketkar

Apress, 2017

ISBN: 9781484227664

Sprache: Englisch

235 Seiten, Download: 6995 KB

Format: PDF, auch als Online-Lesen

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Deep Learning with Python - A Hands-on Introduction

	Contents at a Glance	5
	Contents	6
	About the Author	11
	About the Technical Reviewer	12
	Acknowledgments	13
	Chapter 1: Introduction to Deep Learning	14
	Historical Context	14
	Advances in Related Fields	16
	Prerequisites	16
	Overview of Subsequent Chapters	17
	Installing the Required Libraries	18
	Chapter 2: Machine Learning Fundamentals	19
	Intuition	19
	Binary Classification	19
	Regression	20
	Generalization	21
	Regularization	26
	Summary	28
	Chapter 3: Feed Forward Neural Networks	29
	Unit	29
	Overall Structure of a Neural Network	31
	Expressing the Neural Network in Vector Form	32
	Evaluating the output of the Neural Network	33
	Training the Neural Network	35
	Deriving Cost Functions using Maximum Likelihood	36
	Binary Cross Entropy	37
	Cross Entropy	37
	Squared Error	38
	Summary of Loss Functions	39
	Types of Units/Activation Functions/Layers	39
	Linear Unit	40
	Sigmoid Unit	40
	Softmax Layer	41
	Rectified Linear Unit (ReLU)	41
	Hyperbolic Tangent	42
	Neural Network Hands-on with AutoGrad	45
	Summary	45
	Chapter 4: Introduction to Theano	46
	What is Theano	46
	Theano Hands-On	47
	Summary	72
	Chapter 5: Convolutional Neural Networks	73
	Convolution Operation	73
	Pooling Operation	80
	Convolution-Detector-Pooling Building Block	82
	Convolution Variants	86
	Intuition behind CNNs	87
	Summary	88
	Chapter 6: Recurrent Neural Networks	89
	RNN Basics	89
	Training RNNs	94
	Bidirectional RNNs	101
	Gradient Explosion and Vanishing	102
	Gradient Clipping	103
	Long Short Term Memory	105
	Summary	106
	Chapter 7: Introduction to Keras	107
	Summary	121
	Chapter 8: Stochastic Gradient Descent	122
	Optimization Problems	122
	Method of Steepest Descent	123
	Batch, Stochastic (Single and Mini-batch) Descent	124
	Batch	125
	Stochastic Single Example	125
	Stochastic Mini-batch	125
	Batch vs. Stochastic	125
	Challenges with SGD	125
	Local Minima	125
	Saddle Points	126
	Selecting the Learning Rate	127
	Slow Progress in Narrow Valleys	128
	Algorithmic Variations on SGD	128
	Momentum	129
	Nesterov Accelerated Gradient (NAS)	130
	Annealing and Learning Rate Schedules	130
	Adagrad	130
	RMSProp	131
	Adadelta	132
	Adam	132
	Resilient Backpropagation	132
	Equilibrated SGD	133
	Tricks and Tips for using SGD	133
	Preprocessing Input Data	133
	Choice of Activation Function	133
	Preprocessing Target Value	134
	Initializing Parameters	134
	Shuffling Data	134
	Batch Normalization	134
	Early Stopping	134
	Gradient Noise	134
	Parallel and Distributed SGD	135
	Hogwild	135
	Downpour	135
	Hands-on SGD with Downhill	136
	Summary	141
	Chapter 9: Automatic Differentiation	142
	Numerical Differentiation	142
	Symbolic Differentiation	143
	Automatic Differentiation Fundamentals	144
	Forward/Tangent Linear Mode	145
	Reverse/Cotangent/Adjoint Linear Mode	149
	Implementation of Automatic Differentiation	152
	Source Code Transformation	152
	Operator Overloading	153
	Hands-on Automatic Differentiation with Autograd	154
	Summary	157
	Chapter 10: Introduction to GPUs	158
	Summary	167
	Chapter 11: Introduction to Tensorflow	168
	Summary	203
	Chapter 12: Introduction to PyTorch	204
	Summary	217
	Chapter 13: Regularization Techniques	218
	Model Capacity, Overfitting, and Underfitting	218
	Regularizing the Model	219
	Early Stopping	219
	Norm Penalties	221
	Dropout	222
	Summary	223
	Chapter 14: Training Deep Learning Models	224
	Performance Metrics	224
	Data Procurement	227
	Splitting Data for Training/Validation/Test	228
	Establishing Achievable Limits on the Error Rate	228
	Establishing the Baseline with Standard Choices	229
	Building an Automated, End-to-End Pipeline	229
	Orchestration for Visibility	229
	Analysis of Overfitting and Underfitting	229
	Hyper-Parameter Tuning	231
	Summary	231
	Index	232

Deep Learning with Python - A Hands-on Introduction

von: Nikhil Ketkar

Deep Learning with Python - A Hands-on Introduction

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