The Deep Learning Workshop

封面

版权信息

Preface

1. Building Blocks of Deep Learning

Introduction

Introduction to TensorFlow

Summary

2. Neural Networks

Introduction

Neural Networks and the Structure of Perceptrons

Training a Perceptron

Keras as a High-Level API

Exploring the Optimizers and Hyperparameters of Neural Networks

Activity 2.01: Build a Multilayer Neural Network to Classify Sonar Signals

Summary

3. Image Classification with Convolutional Neural Networks (CNNs)

Introduction

Digital Images

Image Processing

Convolutional Neural Networks

Pooling Layers

Data Augmentation

Saving and Restoring Models

Transfer Learning

Fine-Tuning

Summary

4. Deep Learning for Text – Embeddings

Introduction

Deep Learning for Natural Language Processing

Classical Approaches to Text Representation

Distributed Representation for Text

Summary

5. Deep Learning for Sequences

Introduction

Working with Sequences

Recurrent Neural Networks

Summary

6. LSTMs GRUs and Advanced RNNs

Introduction

Long-Range Dependence/Influence

The Vanishing Gradient Problem

Sequence Models for Text Classification

The Embedding Layer

Building the Plain RNN Model

Making Predictions on Unseen Data

LSTMs GRUs and Other Variants

Parameters in an LSTM

LSTM versus Plain RNNs

Gated Recurrence Units

Bidirectional RNNs

Stacked RNNs

Summarizing All the Models

Attention Models

More Variants of RNNs

Summary

7. Generative Adversarial Networks

Introduction

Deep Convolutional GANs

Summary

Appendix

1. Building Blocks of Deep Learning

2. Neural Networks

3. Image Classification with Convolutional Neural Networks (CNNs)

4. Deep Learning for Text – Embeddings

5. Deep Learning for Sequences

6. LSTMs GRUs and Advanced RNNs