Applied Deep Learning with Keras

封面

版权页

Preface

About the Book

Chapter 1 Introduction to Machine Learning with Keras

Introduction

Data Representation

Data Preprocessing

Machine Learning Libraries

scikit-learn

Keras

Model Training

Model Tuning

Summary

Chapter 2 Machine Learning versus Deep Learning

Introduction

Linear Transformations

Introduction to Keras

Summary

Chapter 3 Deep Learning with Keras

Introduction

Building Your First Neural Network

Model Evaluation

Summary

Chapter 4 Evaluate Your Model with Cross-Validation using Keras Wrappers

Introduction

Cross-Validation

Cross-Validation for Deep Learning Models

Model Selection with Cross-validation

Summary

Chapter 5 Improving Model Accuracy

Introduction

Regularization

L1 and L2 Regularization

Dropout Regularization

Other Regularization Methods

Hyperparameter Tuning with scikit-learn

Summary

Chapter 6 Model Evaluation

Introduction

Accuracy

Imbalanced Datasets

Confusion Matrix

Summary

Chapter 7 Computer Vision with Convolutional Neural Networks

Introduction

Computer Vision

Convolutional Neural Networks

Architecture of a CNN

Image Augmentation

Summary

Chapter 8 Transfer Learning and Pre-Trained Models

Introduction

Pre-Trained Sets and Transfer Learning

Fine-Tuning a Pre-Trained Network

Summary

Chapter 9 Sequential Modeling with Recurrent Neural Networks

Introduction

Sequential Memory and Sequential Modeling

Recurrent Neural Networks (RNNs)

Long Short-Term Memory (LSTM)

Summary

Appendix

Chapter 1: Introduction to Machine Learning with Keras

Chapter 2: Machine Learning versus Deep Learning

Chapter 3: Deep Learning with Keras

Chapter 4: Evaluate Your Model with Cross-Validation with Keras Wrappers

Chapter 5: Improving Model Accuracy

Chapter 6: Model Evaluation

Chapter 7: Computer Vision with Convolutional Neural Networks

Chapter 8: Transfer Learning and Pre-trained Models