Beginning deep learning with TensorFlow : work with Keras, MNIST data sets, and advanced neural networks / Liangqu Long, Xiangming Zeng.

Format:

Book

Author/Creator:

Long, Liangqu, author.

Zeng, Xiangming, author.

Language:

English

Subjects (All):

Machine learning.

TensorFlow.

Physical Description:

1 online resource (727 pages)

Place of Publication:

New York, New York : Apress L. P., [2022]

Summary:

Incorporate deep learning into your development projects through hands-on coding and the latest versions of deep learning software, such as TensorFlow 2 and Keras. The materials used in this book are based on years of successful online education experience and feedback from thousands of online learners. You'll start with an introduction to AI, where you'll learn the history of neural networks and what sets deep learning apart from other varieties of machine learning. Discovery the variety of deep learning frameworks and set-up a deep learning development environment. Next, you'll jump into simple classification programs for hand-writing analysis. Once you've tackled the basics of deep learning, you move on to TensorFlow 2 specifically. Find out what exactly a Tensor is and how to work with MNIST datasets. Finally, you'll get into the heavy lifting of programming neural networks and working with a wide variety of neural network types such as GANs and RNNs. Deep Learning is a new area of Machine Learning research widely used in popular applications, such as voice assistant and self-driving cars. Work through the hands-on material in this book and become a TensorFlow programmer! You will: Develop using deep learning algorithms Build deep learning models using TensorFlow 2 Create classification systems and other, practical deep learning applications.

Contents:

Intro

Table of Contents

About the Authors

About the Technical Reviewer

Acknowledgments

Chapter 1: Introduction to Artificial Intelligence

1.1 Artificial Intelligence in Action

1.1.1 Artificial Intelligence Explained

1.1.2 Machine Learning

1.1.3 Neural Networks and Deep Learning

1.2 The History of Neural Networks

1.2.1 Shallow Neural Networks

1.2.2 Deep Learning

1.3 Deep Learning Characteristics

1.3.1 Data Volume

1.3.2 Computing Power

1.3.3 Network Scale

1.3.4 General Intelligence

1.4 Deep Learning Applications

1.4.1 Computer Vision

1.4.2 Natural Language Processing

1.4.3 Reinforcement Learning

1.5 Deep Learning Framework

1.5.1 Major Frameworks

1.5.2 TensorFlow 2 and 1.x

1.5.3 Demo

1.6 Development Environment Installation

1.6.1 Anaconda Installation

1.6.2 CUDA Installation

1.6.3 TensorFlow Installation

1.6.4 Common Editor Installation

1.7 Summary

1.8 Reference

Chapter 2: Regression

2.1 Neuron Model

2.2 Optimization Method

2.3 Linear Model in Action

2.4 Summary

2.5 References

Chapter 3: Classification

3.1 Handwritten Digital Picture Dataset

3.2 Build a Model

3.3 Error Calculation

3.4 Do We Really Solve the Problem?

3.5 Nonlinear Model

3.6 Model Complexity

3.7 Optimization Method

3.8 Hands-On Handwritten Digital Image Recognition

3.8.1 Build the Network

3.8.2 Model Training

3.9 Summary

3.10 Reference

Chapter 4: Basic TensorFlow

4.1 Data Types

4.1.1 Numeric

4.1.2 String

4.1.3 Boolean

4.2 Numerical Precision

4.3 Tensors to Be Optimized

4.4 Create Tensors

4.4.1 Create Tensors from Arrays and Lists

4.4.2 Create All-0 or All-1 Tensors

4.4.3 Create a Customized Numeric Tensor

4.4.4 Create a Tensor from a Known Distribution

4.4.5 Create a Sequence.

4.5 Typical Applications of Tensors

4.5.1 Scalar

4.5.2 Vector

4.5.3 Matrix

4.5.4 Three-Dimensional Tensor

4.5.5 Four-Dimensional Tensor

4.6 Indexing and Slicing

4.6.1 Indexing

4.6.2 Slicing

4.6.3 Slicing Summary

4.7 Dimensional Transformation

4.7.1 Reshape

4.7.2 Add and Delete Dimensions

4.7.3 Swap Dimensions

4.7.4 Copy Data

4.8 Broadcasting

4.9 Mathematical Operations

4.9.1 Addition, Subtraction, Multiplication and Division

4.9.2 Power Operations

4.9.3 Exponential and Logarithmic Operations

4.9.4 Matrix Multiplication

4.10 Hands-On Forward Propagation

Chapter 5: Advanced TensorFlow

5.1 Merge and Split

5.1.1 Merge

5.1.2 Split

5.2 Common Statistics

5.2.1 Norm

5.2.2 Max, Min, Mean, and Sum

5.3 Tensor Comparison

5.4 Fill and Copy

5.4.1 Fill

5.4.2 Copy

5.5 Data Limiting

5.6 Advanced Operations

5.6.1 tf.gather

5.6.2 tf.gather_nd

5.6.3 tf.boolean_mask

5.6.4 tf.where

5.6.5 tf.scatter_nd

5.6.6 tf.meshgrid

5.7 Load Classic Datasets

5.7.1 Shuffling

5.7.2 Batch Training

5.7.3 Preprocessing

5.7.4 Epoch Training

5.8 Hands-On MNIST Dataset

Chapter 6: Neural Networks

6.1 Perceptron

6.2 Fully Connected Layer

6.2.1 Tensor Mode Implementation

6.2.2 Layer Implementation

6.3 Neural Network

6.3.1 Tensor Mode Implementation

6.3.2 Layer Mode Implementation

6.3.3 Optimization

6.4 Activation function

6.4.1 Sigmoid

6.4.2 ReLU

6.4.3 LeakyReLU

6.4.4 Tanh

6.5 Design of Output Layer

6.5.1 Common Real Number Space

6.5.2 [0, 1] Interval

6.5.3 [0,1] Interval with Sum 1

6.5.4 (-1, 1) Interval

6.6 Error Calculation

6.6.1 Mean Square Error Function

6.6.2 Cross-Entropy Error Function

6.7 Types of Neural Networks

6.7.1 Convolutional Neural Network

6.7.2 Recurrent Neural Network.

6.7.3 Attention Mechanism Network

6.7.4 Graph Convolutional Neural Network

6.8 Hands-On of Automobile Fuel Consumption Prediction

6.8.1 Dataset

6.8.2 Create a Network

6.8.3 Training and Testing

6.9 References

Chapter 7: Backward Propagation Algorithm

7.1 Derivatives and Gradients

7.2 Common Properties of Derivatives

7.2.1 Common Derivatives

7.2.2 Common Property of Derivatives

7.2.3 Hands-On Derivative Finding

7.3 Derivative of Activation Function

7.3.1 Derivative of Sigmoid Function

7.3.2 Derivative of ReLU Function

7.3.3 Derivative of LeakyReLU Function

7.3.4 Derivative of Tanh Function

7.4 Gradient of Loss Function

7.4.1 Gradient of Mean Square Error Function

7.4.2 Gradient of Cross-Entropy Function

7.5 Gradient of Fully Connected Layer

7.5.1 Gradient of a Single Neuron

7.5.2 Gradient of Fully Connected Layer

7.6 Chain Rule

7.7 Back Propagation Algorithm

7.8 Hands-On Optimization of Himmelblau

7.9 Hands-On Back Propagation Algorithm

7.9.1 Dataset

7.9.2 Network Layer

7.9.3 Network model

7.9.4 Network Training

7.9.5 Network Performance

7.10 References

Chapter 8: Keras Advanced API

8.1 Common Functional Modules

8.1.1 Common Network Layer Classes

8.1.2 Network Container

8.2 Model Configuration, Training, and Testing

8.2.1 Model Configuration

8.2.2 Model Training

8.2.3 Model Testing

8.3 Model Saving and Loading

8.3.1 Tensor Method

8.3.2 Network Method

8.3.3 SavedModel method

8.4 Custom Network

8.4.1 Custom Network Layer

8.4.2 Customized Network

8.5 Model Zoo

8.5.1 Load Model

8.6 Metrics

8.6.1 Create a Metrics Container

8.6.2 Write Data

8.6.3 Read Statistical Data

8.6.4 Clear the Container

8.6.5 Hands-On Accuracy Metric

8.7 Visualization

8.7.1 Model Side

8.7.2 Browser Side.

8.8 Summary

Chapter 9: Overfitting

9.1 Model Capacity

9.2 Overfitting and Underfitting

9.2.1 Underfitting

9.2.2 Overfitting

9.3 Dataset Division

9.3.1 Validation Set and Hyperparameters

9.3.2 Early Stopping

9.4 Model Design

9.5 Regularization

9.5.1 L0 Regularization

9.5.2 L1 Regularization

9.5.3 L2 Regularization

9.5.4 Regularization Effect

9.6 Dropout

9.7 Data Augmentation

9.7.1 Rotation

9.7.2 Flip

9.7.3 Cropping

9.7.4 Generate Data

9.7.5 Other Methods

9.8 Hands-On Overfitting

9.8.1 Build the Dataset

9.8.2 Influence of the Number of Network Layers

9.8.3 Impact of Dropout

9.8.4 Impact of Regularization

9.9 References

Chapter 10: Convolutional Neural Networks

10.1 Problems with Fully Connected N

10.1.1 Local Correlation

10.1.2 Weight Sharing

10.1.3 Convolution Operation

10.2 Convolutional Neural Network

10.2.1 Single-Channel Input and Single Convolution Kernel

10.2.2 Multi-channel Input and Single Convolution Kernel

10.2.3 Multi-channel Input and Multi-convolution Kernel

10.2.4 Stride Size

10.2.5 Padding

10.3 Convolutional Layer Implementation

10.3.1 Custom Weights

10.3.2 Convolutional Layer Classes

10.4 Hands-On LeNet-5

10.5 Representation Learning

10.6 Gradient Propagation

10.7 Pooling Layer

10.8 BatchNorm Layer

10.8.1 Forward Propagation

10.8.2 Backward Propagation

10.8.3 Implementation of BatchNormalization layer

10.9 Classical Convolutional Network

10.9.1 AlexNet

10.9.2 VGG Series

10.9.3 GoogLeNet

10.10 Hands-On CIFAR10 and VGG13

10.11 Convolutional Layer Variants

10.11.1 Dilated/Atrous Convolution

10.11.2 Transposed Convolution

o + 2p − k = n * s

o + 2p − k ≠n * s

Matrix Transposition

Transposed Convolution Implementation

10.11.3 Separate Convolution.

10.12 Deep Residual Network

10.12.1 ResNet Principle

10.12.2 ResBlock Implementation

10.13 DenseNet

10.14 Hands-On CIFAR10 and ResNet18

10.15 References

Chapter 11: Recurrent Neural Network

11.1 Sequence Representation Method

11.1.1 Embedding Layer

11.1.2 Pre-trained Word Vectors

11.2 Recurrent Neural Network

11.2.1 Is a Fully Connected Layer Feasible?

11.2.2 Shared Weight

11.2.3 Global Semantics

11.2.4 Recurrent Neural Network

11.3 Gradient Propagation

11.4 How to Use RNN Layers

11.4.1 SimpleRNNCell

11.4.2 Multilayer SimpleRNNCell Network

11.4.3 SimpleRNN Layer

11.5 Hands-On RNN Sentiment Classification

11.5.1 Dataset

11.5.2 Network Model

11.5.3 Training and Testing

11.6 Gradient Vanishing and Gradient Exploding

11.6.1 Gradient Clipping

11.6.2 Gradient Vanishing

11.7 RNN Short-Term Memory

11.8 LSTM Principle

11.8.1 Forget Gate

11.8.2 Input Gate

11.8.3 Update Memory

11.8.4 Output Gate

11.8.5 Summary

11.9 How to Use the LSTM Layer

11.9.1 LSTMCell

11.9.2 LSTM layer

11.10 GRU Introduction

11.10.1 Reset Door

11.10.2 Update Gate

11.10.3 How to Use GRU

11.11 Hands-On LSTM/GRU Sentiment Classification

11.11.1 LSTM Model

11.11.2 GRU model

11.12 Pre-trained Word Vectors

11.13 Pre-trained Word Vectors

11.14 References

Chapter 12: Autoencoder

12.1 Principle of Autoencoder

12.2 Hands-On Fashion MNIST Image Reconstruction

12.2.1 Fashion MNIST Dataset

12.2.2 Encoder

12.2.3 Decoder

12.2.4 Autoencoder

12.2.5 Network Training

12.2.6 Image Reconstruction

12.3 Autoencoder Variants

12.3.1 Dropout Autoencoder

12.3.2 Adversarial Autoencoder

12.4 Variational Autoencoder

12.4.1 Principle of VAE

12.4.2 Reparameterization Trick

12.5 Hands-On VAE Image Reconstruction

12.5.1 VAE model.

12.5.2 Reparameterization Trick.

Notes:

Description based on print version record.

Includes index.

ISBN:

9781523151042

1523151048

9781484279151

1484279158

OCLC:

1294216402