Deep Learning with Python A Hands-on Introduction 无水印pdf.pdf

发布时间：2022-06-15 发布人：admin 分类：说明书资料大小：6.82M 资料格式：pdf 举报版权申诉

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资料目录

Cover

Copyright

Contents at a Glance

Contents

About the Author

1: Introduction to Deep Learning

Historical Context

Advances in Related Fields

Prerequisites

Overview of Subsequent Chapters

Installing the Required Libraries

2: Machine Learning Fundamentals

Intuition

Binary Classification

Regression

Generalization

Regularization

Summary

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 Network

Deriving 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

4: Introduction to Theano

What is Theano

Theano Hands-On

Summary

5: Convolutional Neural Networks

Convolution Operation

Pooling Operation

Convolution-Detector-Pooling Building Block

Convolution Variants

Intuition behind CNNs

Summary

6: Recurrent Neural Networks

RNN Basics

Training RNNs

Bidirectional RNNs

Gradient Explosion and Vanishing

Gradient Clipping

Long Short Term Memory

Summary

7: Introduction to Keras

Summary

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

9: Automatic Differentiation

Numerical Differentiation

Symbolic Differentiation

Automatic Differentiation Fundamentals

Forward/Tangent Linear Mode

Reverse/Cotangent/Adjoint Linear Mode

Implementation of Automatic Differentiation

Source Code Transformation

Operator Overloading

Hands-on Automatic Differentiation with Autograd

Summary

10: Introduction to GPUs

Summary

Index

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