The Deep Learning Workshop
Mirza Rahim Baig Thomas V. Joseph Nipun Sadvilkar Mohan Kumar Silaparasetty Anthony So更新时间：2021-06-11 18:54:14
最新章节：7. Generative Adversarial Networks封面
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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
7. Generative Adversarial Networks
更新时间：2021-06-11 18:54:14