Dự án dịch sách "Dive into Deep Learning"

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Mục lục

• Lời nói đầu
• Cài đặt
• Ký hiệu
• Giới thiệu
• 2. Preliminaries
  • [-] 2.1. Thao tác với Dữ liệu
  • [-] 2.2. Tiền Xử lý Dữ liệu
  • 2.3. Đại số Tuyến tính
  • [-] 2.4. Giải tích
  • [-] 2.5. Tính vi phân Tự động
  • [-] 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 (Fashion-MNIST)
  • 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 Perceptron from Scratch
  • 4.4. Concise Implementation of Multilayer Perceptron
  • 4.5. 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. Considering the Environment
  • 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 Dense Layers to Convolutions
  • 6.2. Convolutions for Images
  • 6.3. Padding and Stride
  • 6.4. Multiple Input and Output Channels
  • 6.5. Pooling
  • 6.6. Convolutional Neural Networks (LeNet)
• 7. Modern Convolutional 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 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
  • 9.8. Beam Search
• 10. Attention Mechanisms
  • 10.1. Attention Mechanisms
  • 10.2. Sequence to Sequence with Attention Mechanisms
  • 10.3. 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
  • 11.6. 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
  • 12.6. 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 (Pikachu)
  • 13.7. Single Shot Multibox Detection (SSD)
  • 13.8. Region-based CNNs (R-CNNs)
  • 13.9. Semantic Segmentation and the Dataset
  • 13.10. Transposed Convolution
  • 13.11. Fully Convolutional Networks (FCN)
  • 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
  • 14.1. Word Embedding (word2vec)
  • 14.2. Approximate Training for Word2vec
  • 14.3. The Dataset for Word2vec
  • 14.4. Implementation of Word2vec
  • 14.5. Subword Embedding
  • 14.6. Word Embedding with Global Vectors (GloVe)
  • 14.7. Finding Synonyms and Analogies
  • 14.8. Sentiment Analysis and the Dataset
  • 14.9. Sentiment Analysis: Using Recurrent Neural Networks
  • 14.10. Sentiment Analysis: Using Convolutional Neural Networks
  • 14.11. Natural Language Inference and the Dataset
• 15. Recommender Systems
  • 15.1. Overview of Recommender Systems
  • 15.2. The MovieLens Dataset
  • 15.3. Matrix Factorization
  • 15.4. AutoRec: Rating Prediction with Autoencoders
  • 15.5. Personalized Ranking for Recommender Systems
  • 15.6. Neural Collaborative Filtering for Personalized Ranking
  • 15.7. Sequence-Aware Recommender Systems
  • 15.8. Feature-Rich Recommender Systems
  • 15.9. Factorization Machines
  • 15.10. Deep Factorization Machines
• 16. Generative Adversarial Networks
  • 16.1. Generative Adversarial Networks
  • 16.2. Deep Convolutional Generative Adversarial Networks
• 17. Appendix: Mathematics for Deep Learning
  • 17.1. Các phép toán Hình Học và Đại Số Tuyến Tính
  • 17.2. Eigendecompositions
  • 17.3. Giải tích một biến
  • 17.4. Multivariable Calculus
  • 17.5. Integral Calculus
  • 17.6. Random Variables
  • 17.7. Maximum Likelihood
  • 17.8. Naive Bayes
  • 17.9. Thống kê
  • 17.10. Information Theory
• 18. Appendix: Tools for Deep Learning
  • 18.1. Using Jupyter
  • 18.2. Using Amazon SageMaker
  • 18.3. Using AWS EC2 Instances
  • 18.4. Using Google Colab
  • 18.5. Selecting Servers and GPUs
  • 18.6. Contributing to This Book
  • 18.7. d2l API Document