17.
Generative Adversarial Networks
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Table Of Contents
Preface
Installation
Notation
1. Introduction
2. Preliminaries
2.1. Data Manipulation
2.2. Data Preprocessing
2.3. Linear Algebra
2.4. Calculus
2.5. Automatic Differentiation
2.6. Probability
2.7. Documentation
3. Linear Neural Networks
3.1. Linear Regression
3.2. Linear Regression Implementation from Scratch
3.3. Concise Implementation of Linear Regression
3.4. Softmax Regression
3.5. The Image Classification Dataset
3.6. Implementation of Softmax Regression from Scratch
3.7. Concise Implementation of Softmax Regression
4. Multilayer Perceptrons
4.1. Multilayer Perceptrons
4.2. Implementation of Multilayer Perceptrons from Scratch
4.3. Concise Implementation of Multilayer Perceptrons
4.4. Model Selection, Underfitting, and Overfitting
4.5. Weight Decay
4.6. Dropout
4.7. Forward Propagation, Backward Propagation, and Computational Graphs
4.8. Numerical Stability and Initialization
4.9. Environment and Distribution Shift
4.10. Predicting House Prices on Kaggle
5. Deep Learning Computation
5.1. Layers and Blocks
5.2. Parameter Management
5.3. Deferred Initialization
5.4. Custom Layers
5.5. File I/O
5.6. GPUs
6. Convolutional Neural Networks
6.1. From Fully-Connected Layers to Convolutions
6.2. Convolutions for Images
6.3. Padding and Stride
6.4. Multiple Input and Multiple Output Channels
6.5. Pooling
6.6. Convolutional Neural Networks (LeNet)
7. Modern Convolutional Neural Networks
7.1. Deep Convolutional Neural Networks (AlexNet)
7.2. Networks Using Blocks (VGG)
7.3. Network in Network (NiN)
7.4. Networks with Parallel Concatenations (GoogLeNet)
7.5. Batch Normalization
7.6. Residual Networks (ResNet)
7.7. Densely Connected Networks (DenseNet)
8. Recurrent Neural Networks
8.1. Sequence Models
8.2. Text Preprocessing
8.3. Language Models and the Dataset
8.4. Recurrent Neural Networks
8.5. Implementation of Recurrent Neural Networks from Scratch
8.6. Concise Implementation of Recurrent Neural Networks
8.7. Backpropagation Through Time
9. Modern Recurrent Neural Networks
9.1. Gated Recurrent Units (GRU)
9.2. Long Short-Term Memory (LSTM)
9.3. Deep Recurrent Neural Networks
9.4. Bidirectional Recurrent Neural Networks
9.5. Machine Translation and the Dataset
9.6. Encoder-Decoder Architecture
9.7. Sequence to Sequence Learning
9.8. Beam Search
10. Attention Mechanisms
10.1. Attention Cues
10.2. Attention Pooling: Nadaraya-Watson Kernel Regression
10.3. Attention Scoring Functions
10.4. Bahdanau Attention
10.5. Multi-Head Attention
10.6. Self-Attention and Positional Encoding
10.7. Transformer
11. Optimization Algorithms
11.1. Optimization and Deep Learning
11.2. Convexity
11.3. Gradient Descent
11.4. Stochastic Gradient Descent
11.5. Minibatch Stochastic Gradient Descent
11.6. Momentum
11.7. Adagrad
11.8. RMSProp
11.9. Adadelta
11.10. Adam
11.11. Learning Rate Scheduling
12. Computational Performance
12.1. Compilers and Interpreters
12.2. Asynchronous Computation
12.3. Automatic Parallelism
12.4. Hardware
12.5. Training on Multiple GPUs
12.6. Concise Implementation for Multiple GPUs
12.7. Parameter Servers
13. Computer Vision
13.1. Image Augmentation
13.2. Fine-Tuning
13.3. Object Detection and Bounding Boxes
13.4. Anchor Boxes
13.5. Multiscale Object Detection
13.6. The Object Detection Dataset
13.7. Single Shot Multibox Detection
13.8. Region-based CNNs (R-CNNs)
13.9. Semantic Segmentation and the Dataset
13.10. Transposed Convolution
13.11. Fully Convolutional Networks
13.12. Neural Style Transfer
13.13. Image Classification (CIFAR-10) on Kaggle
13.14. Dog Breed Identification (ImageNet Dogs) on Kaggle
14. Natural Language Processing: Pretraining
14.1. Word Embedding (word2vec)
14.2. Approximate Training
14.3. The Dataset for Pretraining Word Embeddings
14.4. Pretraining word2vec
14.5. Word Embedding with Global Vectors (GloVe)
14.6. Subword Embedding
14.7. Word Similarity and Analogy
14.8. Bidirectional Encoder Representations from Transformers (BERT)
14.9. The Dataset for Pretraining BERT
14.10. Pretraining BERT
15. Natural Language Processing: Applications
15.1. Sentiment Analysis and the Dataset
15.2. Sentiment Analysis: Using Recurrent Neural Networks
15.3. Sentiment Analysis: Using Convolutional Neural Networks
15.4. Natural Language Inference and the Dataset
15.5. Natural Language Inference: Using Attention
15.6. Fine-Tuning BERT for Sequence-Level and Token-Level Applications
15.7. Natural Language Inference: Fine-Tuning BERT
16. Recommender Systems
16.1. Overview of Recommender Systems
16.2. The MovieLens Dataset
16.3. Matrix Factorization
16.4. AutoRec: Rating Prediction with Autoencoders
16.5. Personalized Ranking for Recommender Systems
16.6. Neural Collaborative Filtering for Personalized Ranking
16.7. Sequence-Aware Recommender Systems
16.8. Feature-Rich Recommender Systems
16.9. Factorization Machines
16.10. Deep Factorization Machines
17. Generative Adversarial Networks
17.1. Generative Adversarial Networks
17.2. Deep Convolutional Generative Adversarial Networks
18. Appendix: Mathematics for Deep Learning
18.1. Geometry and Linear Algebraic Operations
18.2. Eigendecompositions
18.3. Single Variable Calculus
18.4. Multivariable Calculus
18.5. Integral Calculus
18.6. Random Variables
18.7. Maximum Likelihood
18.8. Distributions
18.9. Naive Bayes
18.10. Statistics
18.11. Information Theory
19. Appendix: Tools for Deep Learning
19.1. Using Jupyter
19.2. Using Amazon SageMaker
19.3. Using AWS EC2 Instances
19.4. Using Google Colab
19.5. Selecting Servers and GPUs
19.6. Contributing to This Book
19.7.
d2l
API Document
References
Table Of Contents
Preface
Installation
Notation
1. Introduction
2. Preliminaries
2.1. Data Manipulation
2.2. Data Preprocessing
2.3. Linear Algebra
2.4. Calculus
2.5. Automatic Differentiation
2.6. Probability
2.7. Documentation
3. Linear Neural Networks
3.1. Linear Regression
3.2. Linear Regression Implementation from Scratch
3.3. Concise Implementation of Linear Regression
3.4. Softmax Regression
3.5. The Image Classification Dataset
3.6. Implementation of Softmax Regression from Scratch
3.7. Concise Implementation of Softmax Regression
4. Multilayer Perceptrons
4.1. Multilayer Perceptrons
4.2. Implementation of Multilayer Perceptrons from Scratch
4.3. Concise Implementation of Multilayer Perceptrons
4.4. Model Selection, Underfitting, and Overfitting
4.5. Weight Decay
4.6. Dropout
4.7. Forward Propagation, Backward Propagation, and Computational Graphs
4.8. Numerical Stability and Initialization
4.9. Environment and Distribution Shift
4.10. Predicting House Prices on Kaggle
5. Deep Learning Computation
5.1. Layers and Blocks
5.2. Parameter Management
5.3. Deferred Initialization
5.4. Custom Layers
5.5. File I/O
5.6. GPUs
6. Convolutional Neural Networks
6.1. From Fully-Connected Layers to Convolutions
6.2. Convolutions for Images
6.3. Padding and Stride
6.4. Multiple Input and Multiple Output Channels
6.5. Pooling
6.6. Convolutional Neural Networks (LeNet)
7. Modern Convolutional Neural Networks
7.1. Deep Convolutional Neural Networks (AlexNet)
7.2. Networks Using Blocks (VGG)
7.3. Network in Network (NiN)
7.4. Networks with Parallel Concatenations (GoogLeNet)
7.5. Batch Normalization
7.6. Residual Networks (ResNet)
7.7. Densely Connected Networks (DenseNet)
8. Recurrent Neural Networks
8.1. Sequence Models
8.2. Text Preprocessing
8.3. Language Models and the Dataset
8.4. Recurrent Neural Networks
8.5. Implementation of Recurrent Neural Networks from Scratch
8.6. Concise Implementation of Recurrent Neural Networks
8.7. Backpropagation Through Time
9. Modern Recurrent Neural Networks
9.1. Gated Recurrent Units (GRU)
9.2. Long Short-Term Memory (LSTM)
9.3. Deep Recurrent Neural Networks
9.4. Bidirectional Recurrent Neural Networks
9.5. Machine Translation and the Dataset
9.6. Encoder-Decoder Architecture
9.7. Sequence to Sequence Learning
9.8. Beam Search
10. Attention Mechanisms
10.1. Attention Cues
10.2. Attention Pooling: Nadaraya-Watson Kernel Regression
10.3. Attention Scoring Functions
10.4. Bahdanau Attention
10.5. Multi-Head Attention
10.6. Self-Attention and Positional Encoding
10.7. Transformer
11. Optimization Algorithms
11.1. Optimization and Deep Learning
11.2. Convexity
11.3. Gradient Descent
11.4. Stochastic Gradient Descent
11.5. Minibatch Stochastic Gradient Descent
11.6. Momentum
11.7. Adagrad
11.8. RMSProp
11.9. Adadelta
11.10. Adam
11.11. Learning Rate Scheduling
12. Computational Performance
12.1. Compilers and Interpreters
12.2. Asynchronous Computation
12.3. Automatic Parallelism
12.4. Hardware
12.5. Training on Multiple GPUs
12.6. Concise Implementation for Multiple GPUs
12.7. Parameter Servers
13. Computer Vision
13.1. Image Augmentation
13.2. Fine-Tuning
13.3. Object Detection and Bounding Boxes
13.4. Anchor Boxes
13.5. Multiscale Object Detection
13.6. The Object Detection Dataset
13.7. Single Shot Multibox Detection
13.8. Region-based CNNs (R-CNNs)
13.9. Semantic Segmentation and the Dataset
13.10. Transposed Convolution
13.11. Fully Convolutional Networks
13.12. Neural Style Transfer
13.13. Image Classification (CIFAR-10) on Kaggle
13.14. Dog Breed Identification (ImageNet Dogs) on Kaggle
14. Natural Language Processing: Pretraining
14.1. Word Embedding (word2vec)
14.2. Approximate Training
14.3. The Dataset for Pretraining Word Embeddings
14.4. Pretraining word2vec
14.5. Word Embedding with Global Vectors (GloVe)
14.6. Subword Embedding
14.7. Word Similarity and Analogy
14.8. Bidirectional Encoder Representations from Transformers (BERT)
14.9. The Dataset for Pretraining BERT
14.10. Pretraining BERT
15. Natural Language Processing: Applications
15.1. Sentiment Analysis and the Dataset
15.2. Sentiment Analysis: Using Recurrent Neural Networks
15.3. Sentiment Analysis: Using Convolutional Neural Networks
15.4. Natural Language Inference and the Dataset
15.5. Natural Language Inference: Using Attention
15.6. Fine-Tuning BERT for Sequence-Level and Token-Level Applications
15.7. Natural Language Inference: Fine-Tuning BERT
16. Recommender Systems
16.1. Overview of Recommender Systems
16.2. The MovieLens Dataset
16.3. Matrix Factorization
16.4. AutoRec: Rating Prediction with Autoencoders
16.5. Personalized Ranking for Recommender Systems
16.6. Neural Collaborative Filtering for Personalized Ranking
16.7. Sequence-Aware Recommender Systems
16.8. Feature-Rich Recommender Systems
16.9. Factorization Machines
16.10. Deep Factorization Machines
17. Generative Adversarial Networks
17.1. Generative Adversarial Networks
17.2. Deep Convolutional Generative Adversarial Networks
18. Appendix: Mathematics for Deep Learning
18.1. Geometry and Linear Algebraic Operations
18.2. Eigendecompositions
18.3. Single Variable Calculus
18.4. Multivariable Calculus
18.5. Integral Calculus
18.6. Random Variables
18.7. Maximum Likelihood
18.8. Distributions
18.9. Naive Bayes
18.10. Statistics
18.11. Information Theory
19. Appendix: Tools for Deep Learning
19.1. Using Jupyter
19.2. Using Amazon SageMaker
19.3. Using AWS EC2 Instances
19.4. Using Google Colab
19.5. Selecting Servers and GPUs
19.6. Contributing to This Book
19.7.
d2l
API Document
References
17.
Generative Adversarial Networks
¶
17.1. Generative Adversarial Networks
17.1.1. Generate Some “Real” Data
17.1.2. Generator
17.1.3. Discriminator
17.1.4. Training
17.1.5. Summary
17.1.6. Exercises
17.2. Deep Convolutional Generative Adversarial Networks
17.2.1. The Pokemon Dataset
17.2.2. The Generator
17.2.3. Discriminator
17.2.4. Training
17.2.5. Summary
17.2.6. Exercises
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17.1. Generative Adversarial Networks
