Dự án dịch sách "Dive into Deep Learning"
Cuốn sách này được dịch và đăng tại https://d2l.aivivn.com/.
Với các mục con (2.1, 2.2, ...)
- Đã dịch xong
- [-] Đang dịch
- Chưa bắt đầu
Với các chương (2., 3., ...)
- Chưa revise
- [-] Đang revise
- Đã revise xong.
- 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