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d2l-ai/d2l-zh

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75,708 stars·12,129 forks·Python·apache-2.0·3 viewszh.d2l.ai↗

D2l Zh

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Features

  • Recurrent Neural Networks - Models sequential dependencies in data through clear, code-based implementations of recurrent neural network structures.
  • Transformer - Explains the underlying attention mechanisms and architectural design choices that power modern transformer models.
  • Attention Mechanisms - Demonstrates how to calculate weighted relationships between data segments to maintain focus and logical consistency within neural models.
  • Automatic Differentiation Systems - Utilizes computational graphs to automatically derive gradients for neural network training.
  • Neural Network Layers - Exposes core architectural building blocks, including dense and convolutional layers, for assembling deep learning models.
  • Machine Learning Curricula - Maps out a structured path from fundamental concepts to advanced neural network architectures using practical, code-first lessons.
  • Interactive Textbooks - Combines theoretical explanations with executable code blocks to enable hands-on experimentation with complex machine learning concepts.
  • Machine Learning Tutorials - Walks learners through the practical steps of training algorithms, processing datasets, and building predictive models.
  • Learning Management Systems - Facilitates the distribution of deep learning curriculum and tracks progress through an interactive, browser-based educational environment.
  • Automatic Differentiation - Applies computational techniques to evaluate derivatives of mathematical functions within deep learning workflows.
  • Multilayer Perceptrons - Connects neurons across multiple stages using non-linear activation functions to process complex input data.
  • Deep Learning Curricula - Delivers a comprehensive, code-first curriculum that guides students through both foundational and advanced deep learning topics.
  • Interactive Technical Documentation - Allows users to modify and execute code directly within the documentation to verify technical concepts in real time.
  • Hybrid Execution Modes - Balances the flexibility of imperative programming with the performance benefits of symbolic graph compilation.
  • Convolutional Neural Networks - Examines the structural components and feature extraction capabilities of convolutional neural networks through interactive code examples.
  • Linear Regression Implementations - Builds foundational knowledge by implementing linear regression models from scratch using code-first examples.
  • Sequence Models - Examines the theoretical and practical aspects of processing ordered data where temporal dependencies are significant.
  • Model Performance Optimizations - Teaches software abstractions and optimization techniques to maximize the computational efficiency of deep learning models.
  • Data-Parallel Training - Integrates practical strategies for synchronizing gradients across hardware units to enable efficient data-parallel model training.
  • Computational Compilers - Investigates how high-level model definitions are compiled into optimized execution graphs for hardware acceleration.
  • Optimization Algorithms - Breaks down the mathematics and code behind the optimization algorithms essential for minimizing loss during training.
  • AdaGrad Optimizers - Guides the implementation of gradient descent variants that adapt learning rates based on parameter frequency.
  • Adam Optimizers - Features implementations of adaptive moment estimation to optimize stochastic objective functions.
  • Gradient Descent Algorithms - Details the iterative mechanics of updating model parameters by following negative gradients.
  • Momentum Optimizers - Explains how momentum-based optimization algorithms accelerate convergence by smoothing gradient updates during the model training process.
  • RMSProp Optimizers - Implements adaptive learning rate strategies using moving averages of squared gradients in a tutorial format.
  • Machine Learning Prototyping - Supplies interactive environments and modular code snippets to accelerate the development of experimental model architectures.
  • Model Training Pipelines - Organizes end-to-end workflows that manage data sourcing, model training, and performance validation.
  • Convolutional Operations - Clarifies the mathematical operations behind padding, strides, and kernels used to process spatial data.
  • Network in Network Architectures - Details the use of micro-networks within convolutional layers to improve feature abstraction capabilities.
  • Pooling Layers - Illustrates the theory and implementation of downsampling operations used to reduce spatial dimensions in neural networks.
  • Recurrent Neural Networks Tutorials - Presents clear, step-by-step instructions for building and understanding recurrent neural network layers.
  • Regularization Techniques - Mitigates overfitting in deep neural networks by teaching weight decay and dropout techniques.
  • Asynchronous Task Schedulers - Highlights techniques for decoupling host-side execution from device-side processing to maximize hardware efficiency.
  • Lazy Parameter Initializations - Defers weight allocation until the first forward pass to enable dynamic shape inference based on input data.
  • Object Detection Tutorials - Covers the implementation of object detection algorithms and bounding box regression through interactive coding modules.
  • Language Model Pretraining - Explores strategies for pretraining language models on massive text corpora before fine-tuning for specific tasks.
  • Word Embeddings - Guides learners through constructing vector representations that capture semantic relationships between words.
  • Computer Vision Curations - Curates educational materials and practical examples focused on the theory and application of computer vision systems.
  • Learning Rate Schedulers - Provides code implementations and explanations for algorithms that adjust learning rates to improve model convergence.
  • Transformer Model Tutorials - Unpacks the mechanisms behind transformer-based models for interpreting complex text data through structured lessons.
  • Linear Regression Tutorials - Presents step-by-step tutorials on building and evaluating linear regression models as a starting point for deep learning.
  • Convolutional Neural Network Tutorials - Examines the evolution and practical implementation of landmark convolutional architectures through guided tutorials.
  • Linear Neural Networks - Covers foundational concepts including loss functions, data preprocessing, and the architecture design of linear neural networks.
  • Recurrent Neural Network Tutorials - Breaks down the complexities of sequential data processing through hands-on recurrent neural network implementation exercises.
  • Transfer Learning - Highlights techniques for adapting pre-trained language models to specific downstream tasks via fine-tuning methods.
  • Deep Learning Computation Tutorials - Offers a structured curriculum for understanding the software abstractions and computational requirements of deep learning systems.
  • Object Detection and Tracking - Details modern algorithmic approaches for identifying and tracking objects within complex visual environments.
  • Adaptive Learning Rate Optimizers - Analyzes how adaptive learning rate optimizers dynamically adjust parameter updates to improve training stability and convergence speed.
  • Batch Normalization - Investigates the role of batch normalization in stabilizing deep network training by regulating internal layer activations.
  • Computer Vision - Explores the application of deep learning architectures to solve diverse computer vision challenges.
  • Image Augmentation - Enforces data diversity in computer vision pipelines by applying random transformations to training images.
  • Object Detection Models - Coordinates the implementation of neural network architectures tailored for identifying and locating objects within visual data.
  • Asynchronous Computations - Parses the complexities of overlapping computation and data transfer to improve model training throughput.
  • Hardware Acceleration - Discusses optimization techniques for leveraging hardware acceleration to improve throughput in large-scale model training.
  • Attention Scoring Functions - Defines the mathematical scoring functions used to calculate relevance within attention-based neural architectures.
  • Bidirectional Recurrent Neural Networks - Details the architecture and implementation of bidirectional recurrent neural networks for advanced sequence processing.
  • Image Convolutions - Explains the mathematical operations behind image convolutions used to preserve spatial structures in visual data.
  • Numerical Stability and Initialization - Examines weight initialization strategies and regularization methods to maintain stable gradient flow during the training of deep neural networks.
  • Image Classification Datasets - Supplies foundational training data and methodologies for developing robust image recognition models.
  • Natural Language Processing Datasets - Includes diverse text corpora and structured data for training and evaluating natural language processing systems.
  • Object Detection Datasets - Organizes annotated datasets specifically designed for object detection and localization tasks in computer vision.
  • NLP Applications - Demonstrates practical implementations of various natural language processing tasks using modern deep learning architectures.
  • Subword Embeddings - Demonstrates subword tokenization strategies to effectively manage out-of-vocabulary terms in natural language processing models.
  • Executable Documentation - Integrates technical explanations with live code blocks to enable immediate experimentation within the documentation.
  • Word Embedding Datasets - Provides curated datasets and processing pipelines for training and evaluating semantic word embeddings.
  • Array and Tensor Manipulation - Wraps complex tensor operations into accessible tutorials for reshaping and transforming multidimensional data.
  • Neural Network Implementations - Supplies complete, from-scratch implementations of various neural network architectures to demonstrate core training mechanics.
  • Interactive Notebooks - Employs executable notebooks to provide an immersive environment for experimenting with code and model parameters.
  • Data Preprocessing Tutorials - Covers essential techniques for cleaning, manipulating, and preparing large-scale datasets for machine learning workflows.
  • Natural Language Inference Tutorials - Explains the mechanics of natural language inference through interactive code examples and theoretical breakdowns.
  • Natural Language Processing Tutorials - Provides structured lessons on attention mechanisms and complex architectures for solving natural language processing problems.
  • Convolutional Neural Network Architectures - Examines the evolution of convolutional neural network architectures through historical and modern design implementations.
  • Sentiment Analysis Models - Showcases implementation patterns for neural networks designed to perform sentiment classification on text data.
  • Softmax Regression - Presents the mathematical foundations and implementation steps for building softmax regression models from scratch.
  • Automatic Parallelism Tutorials - Optimizes training performance by teaching techniques for asynchronous computation and multi-device parallel execution.
  • Deferred Initialization Tutorials - Offers insights into how modern deep learning libraries abstract complex operations to accelerate model prototyping.
  • Parameter Management Tutorials - Guides learners through the process of managing model parameters using modern deep learning software frameworks.
  • Linear Algebra - Catalogs fundamental matrix and vector operations required for data manipulation in deep learning.
  • Semantic Segmentation Datasets - Features curated datasets and methodologies for training models on pixel-level image classification tasks.
  • Parameter Servers - Outlines distributed architecture patterns such as parameter servers that facilitate scaling deep learning workloads.

    • This project is an open-source, interactive educational platform designed to teach deep learning through a comprehensive, code-first curriculum. It provides a structured learning path that covers foundational mathematics, modern neural network architectures, and practical optimization techniques, enabling practitioners to master complex artificial intelligence concepts through hands-on experimentation.

    The platform distinguishes itself by integrating technical explanations with executable Jupyter notebooks. This design allows readers to modify code and hyperparameters in real-time, facilitating immediate feedback and practical skill acquisition. The curriculum spans a wide range of domains, including computer vision and natural language processing, while providing the necessary infrastructure to run these interactive materials locally or via cloud-based environments.

    The project covers a broad capability surface, including end-to-end model training pipelines, advanced sequence modeling, and techniques for computational performance optimization. It addresses essential deep learning primitives such as automatic differentiation, layer construction, and parameter management, ensuring users gain both theoretical understanding and implementation proficiency.

    The documentation is structured as a live, interactive textbook, with comprehensive guides for environment setup and cloud resource management to support the learning experience.