The Deep Learning with Keras Workshop

封面

版权信息

Preface

1. Introduction to Machine Learning with Keras

Introduction

Data Representation

Data Preprocessing

Life Cycle of Model Creation

scikit-learn

Keras

Model Training

Model Tuning

Summary

2. Machine Learning versus Deep Learning

Introduction

Linear Transformations

Introduction to Keras

Summary

3. Deep Learning with Keras

Introduction

Building Your First Neural Network

Model Evaluation

Summary

4. Evaluating Your Model with Cross-Validation Using Keras Wrappers

Introduction

Cross-Validation

Cross-Validation for Deep Learning Models

Model Selection with Cross-Validation

Summary

5. Improving Model Accuracy

Introduction

Regularization

L1 and L2 Regularization

Dropout Regularization

Other Regularization Methods

Hyperparameter Tuning with scikit-learn

Summary

6. Model Evaluation

Introduction

Accuracy

Imbalanced Datasets

Confusion Matrix

Summary

7. Computer Vision with Convolutional Neural Networks

Introduction

Computer Vision

Convolutional Neural Networks

The Architecture of a CNN

Image Augmentation

Summary

8. Transfer Learning and Pre-Trained Models

Introduction

Pre-Trained Sets and Transfer Learning

Fine-Tuning a Pre-Trained Network

Summary

9. Sequential Modeling with Recurrent Neural Networks

Introduction

Sequential Memory and Sequential Modeling

Recurrent Neural Networks (RNNs)

Long Short-Term Memory (LSTM)

Summary

Appendix

1. Introduction to Machine Learning with Keras

2. Machine Learning versus Deep Learning

3. Deep Learning with Keras

4. Evaluating Your Model with Cross-Validation Using Keras Wrappers

5. Improving Model Accuracy

6. Model Evaluation

7. Computer Vision with Convolutional Neural Networks

8. Transfer Learning and Pre-Trained Models