yunjey/pytorch-tutorial

Features

• Neural Network Implementations - Implements a wide array of neural network architectures from basic linear regression to GANs.
• Deep Learning Education - Provides a comprehensive collection of educational resources for learning neural network implementation and training.
• Automatic Differentiation Engines - Implements the automatic differentiation engine required for backpropagation in deep learning.
• Computational Graphs - Utilizes tensor-based computational graphs to automate gradient calculations.
• Computer Vision Models - Implements diverse computer vision models including convolutional and residual networks for classification and style transfer.
• Convolutional Neural Networks - Implements convolutional neural networks for image classification and feature extraction.
• Deep Learning Tutorials - Serves as a comprehensive instructional resource for implementing deep learning architectures and frameworks using PyTorch.
• Layered Architectures - Uses a layered architecture to stack convolutional, linear, and recurrent components modularly.
• Weight Optimizers - Implements weight optimizers like SGD and Adam to minimize loss via gradient descent.
• Natural Language Processing - Develops sequence models and language predictors to process text and generate captions.
• Neural Network Implementation Guides - Provides step-by-step guides for translating mathematical concepts into convolutional, recurrent, and residual network code.
• Neural Training Pipelines - Implements the full neural training pipeline from data loading to weight optimization.
• Training Execution Loops - Provides the core training execution loops for performing forward passes and weight optimization.
• Recurrent Neural Networks - Implements recurrent neural networks using LSTM units for sequential data processing.
• Forward Pass Logic - Implements the forward pass logic to propagate input data through network layers for inference.
• Generative Models - Implements generative models, including adversarial and variational networks, for visual synthesis.
• Image Description Generation - Generates natural language descriptions of visual content by decoding image feature vectors.
• Language Model Architectures - Implements language model architectures to predict and sample text from a training corpus.
• Logistic Regression Models - Provides an implementation of logistic regression models for binary classification using weight optimization.
• Data Preprocessing - Provides data preprocessing methods for cleaning and transforming datasets before training.
• Model Checkpointing - Implements mechanisms for saving and restoring neural network states and weights during training.
• Model Performance Visualizations - Generates graphical representations of loss curves and gradients to diagnose model convergence.
• Feedforward Architectures - Constructs multi-layer perceptrons with linear layers and activation functions for structured data classification.
• Neural Style Transfer - Implements neural style transfer to blend content and artistic style in images.
• Cross-Entropy Loss Functions - Implements cross-entropy loss functions to guide the training of classification models.
• Residual Networks - Implements residual networks using shortcut connections to enable deeper model training.
• Sequence Modeling - Develops sequence modeling architectures to process ordered data and generate patterns.
• Batched Data Loading - Implements batched data loading to handle large datasets efficiently during training.
• Model State Persistence - Provides capabilities for saving and loading model weights and optimizer states for training resumption.
• Training Visualization Interfaces - Ships visual dashboards to track and display real-time performance metrics during model training.
• Computer Vision Tutorials - Offers practical educational examples for implementing image classification and neural style transfer.
• Generative Model Examples - Provides implementations of adversarial and variational networks to synthesize or transform visual content.
• Training Metrics - Implements logging and visualization of performance indicators like loss, accuracy, and gradients.
• Model Evaluation - Includes tools for measuring model accuracy and performance on test datasets to verify generalization.
• Deep Learning Tutorials - Beginner-friendly tutorials for deep learning with PyTorch.
• Learning Resources - High-quality introductory tutorials for deep learning development.
• Tutorials - Listed in the “Tutorials” section of the The Incredible Pytorch awesome list.

Star history