wangshusen/DeepLearning
DeepLearning
This is an educational repository providing implementations and tutorials for deep learning, neural network architectures, and machine learning fundamentals. It serves as a reference for building multilayer perceptrons, convolutional networks, and recurrent networks using backpropagation and gradient descent.
The project includes specialized frameworks for generative modeling via autoencoders and generative adversarial networks, as well as a toolkit for reinforcement learning that implements value-based, policy-based, and actor-critic methods. It also provides practical references for transformer and BERT architectures, focusing on attention mechanisms for natural language processing and visual data tasks.
The repository covers a broad range of capabilities, including computer vision processing, sequence modeling, and adversarial robustness analysis. It also provides guides for distributed machine learning, detailing strategies for scaling training across multiple nodes using MapReduce, parameter servers, and federated learning.
The project provides foundational support for traditional machine learning algorithms, specifically covering regression, classification, and clustering.
Features
- Neural Network Construction - Provides a comprehensive guide and implementations for designing and building deep learning architectures like MLPs and CNNs.
- Convolutional Feature Extraction - Implements convolutional filters and pooling layers to extract spatial hierarchies of visual patterns from images.
- Deep Reinforcement Learning Implementations - Implements advanced reinforcement learning algorithms including value-based, policy-based, and actor-critic methods.
- Generative Models - Implements generative architectures such as autoencoders and generative adversarial networks for synthetic data and image generation.
- Machine Learning Implementations - Provides code-based implementations of foundational machine learning algorithms including regression, classification, and clustering.
- Computer Vision - Implements convolutional neural networks and normalization techniques for image pattern analysis and processing.
- Neural Network Implementation Guides - Offers practical guides for translating mathematical neural network concepts into code implementations from scratch.
- Sequential Data Models - Uses recurrent neural networks and transformer architectures to process natural language and generate text sequences.
- Transformer Architecture Implementation - Provides practical implementations of transformer and BERT architectures using self-attention mechanisms for text and visual tasks.
- Backpropagation Training - Provides foundational implementations of backpropagation for computing gradients and optimizing neural network weights.
- Deep Learning Education - Serves as a curated educational resource for learning neural network theory and practical implementation.
- Machine Learning Fundamentals - Provides educational content and implementations for fundamental machine learning algorithms including regression, classification, and clustering.
- Actor-Critic Architectures - Implements actor-critic architectures that combine policy-based agents with value-based evaluators to stabilize reinforcement learning training.
- Input Sequence Attentions - Implements attention mechanisms that weight input sequence positions to capture long-range dependencies in text data.
- Distributed Deep Learning - Details strategies for scaling deep learning training across multiple nodes using parameter servers and federated learning.
- Distributed Training Guides - Provides strategic guides for scaling model training using MapReduce, parameter servers, and federated learning.
- Distributed Learning - Implements strategies for scaling model training across multiple nodes using MapReduce, parameter servers, and federated learning.
- Transformer Architectures - Implements transformer and BERT architectures using attention mechanisms for NLP and visual data tasks.
- Generative Adversarial Networks - Provides implementations of generative adversarial networks using competing generator and discriminator networks to synthesize data.
- Parameter Servers - Implements parameter server architectures to synchronize model gradients across multiple computing nodes.
- Reinforcement Learning Implementations - Implements reinforcement learning agents using value-based, policy-based methods and Monte Carlo tree search.
- MapReduce Processing Engines - Implements MapReduce processing for splitting large datasets into chunks to accelerate parallel model training.
- Generative Model Examples - Provides practical examples of autoencoders and generative adversarial networks for synthetic image generation.
Star history
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