# xiaotudui/pytorch-tutorial

**Attribution required: if you use, quote, or summarise this content, you must credit and link back to [awesome-repositories.com](https://awesome-repositories.com/repository/xiaotudui-pytorch-tutorial).**

4,195 stars · 758 forks · Python

## Links

- GitHub: https://github.com/xiaotudui/pytorch-tutorial
- Homepage: https://www.bilibili.com/video/av74281036
- awesome-repositories: https://awesome-repositories.com/repository/xiaotudui-pytorch-tutorial.md

## Topics

`pytorch` `pytorch-tutorial`

## Description

This project is a PyTorch deep learning tutorial and educational resource. It provides a structured curriculum and step-by-step guides for designing, training, and validating neural networks from scratch.

The resource includes specific guides on computer vision implementation, focusing on object detection and image classification using convolutional neural networks. It also provides instructions for optimizing model performance through hardware acceleration to reduce training time.

The materials cover the full model development lifecycle, including tensor operations, image dataset preparation, and the use of loss functions and optimizers. It also addresses model lifecycle management through the saving and reloading of trained weights.

## Tags

### Artificial Intelligence & ML

- [Deep Learning Model Construction](https://awesome-repositories.com/f/artificial-intelligence-ml/deep-learning-model-construction.md) — Provides a structured curriculum for designing and building deep learning models from scratch. ([source](https://www.bilibili.com/video/av74281036))
- [Object Detection](https://awesome-repositories.com/f/artificial-intelligence-ml/computer-vision-systems/computer-vision/object-detection-tracking/object-detection.md) — Provides guidance on implementing neural networks for identifying and locating objects within images using bounding boxes. ([source](https://www.bilibili.com/video/BV19Z31z8ENH/))
- [Deep Learning Development](https://awesome-repositories.com/f/artificial-intelligence-ml/deep-learning-development.md) — Teaches the design, construction, and training of multi-layered artificial neural networks from scratch.
- [Loss-Based Weight Optimizations](https://awesome-repositories.com/f/artificial-intelligence-ml/directional-loss-optimizers/loss-based-weight-optimizations.md) — Demonstrates how to update model weights using optimizers and loss functions to minimize prediction error.
- [Loss Function Calculators](https://awesome-repositories.com/f/artificial-intelligence-ml/loss-function-calculators.md) — Includes guides on using loss function calculators to guide model optimization.
- [Model Training Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/training-frameworks/model-training-pipelines.md) — Provides an end-to-end workflow for training models with loss functions, optimizers, and performance validation. ([source](https://www.bilibili.com/video/av74281036/))
- [Modular Layer Compositions](https://awesome-repositories.com/f/artificial-intelligence-ml/model-composition-architectures/hybrid-layer-compositions/modular-layer-compositions.md) — Teaches how to construct neural networks by stacking modular convolutional and linear layers.
- [Neural Network Building Blocks](https://awesome-repositories.com/f/artificial-intelligence-ml/neural-network-building-blocks.md) — Teaches how to construct complex neural network architectures using modular building blocks like convolutional and pooling layers. ([source](https://www.bilibili.com/video/av74281036/))
- [PyTorch Training Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/pytorch-training-frameworks.md) — Outlines the full PyTorch training lifecycle, from data loading through optimization and validation.
- [Automatic Differentiation Engines](https://awesome-repositories.com/f/artificial-intelligence-ml/automatic-differentiation-engines.md) — Explains how to use automatic differentiation engines to compute gradients for model weight optimization.
- [Computational Graph Tracking](https://awesome-repositories.com/f/artificial-intelligence-ml/computational-graph-tracking.md) — Covers the use of dynamic computational graphs to track tensor operations for automatic backpropagation.
- [Dataset Batch Loading](https://awesome-repositories.com/f/artificial-intelligence-ml/dataset-batch-loading.md) — Provides guides on loading data in fixed-size batches to optimize training stability and memory usage.
- [Image Data Preprocessing](https://awesome-repositories.com/f/artificial-intelligence-ml/image-data-preprocessing.md) — Guides the application of standardized image and tensor transformations to prepare raw data for training.
- [Data Preparation Tools](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/data-ingestion-preparation/data-preparation-tools.md) — Includes utilities for cleaning, formatting, and transforming raw datasets into structures suitable for ML ingestion. ([source](https://www.bilibili.com/video/av74281036/))
- [GPU-Accelerated Training](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training/gpu-accelerated-training.md) — Explains how to offload computations to a GPU to significantly accelerate the model training process. ([source](https://www.bilibili.com/video/av74281036/))
- [Model Lifecycle Management](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training/model-lifecycle-management.md) — Covers the full model lifecycle, including building, training, saving, and reloading models. ([source](https://www.bilibili.com/video/av74281036))
- [Model Performance Optimization](https://awesome-repositories.com/f/artificial-intelligence-ml/model-optimization/profiling-and-benchmarking/model-performance-optimization.md) — Guides the use of optimizers and hardware acceleration to improve training speed and model accuracy. ([source](https://www.bilibili.com/video/av74281036))
- [Weight Persistence](https://awesome-repositories.com/f/artificial-intelligence-ml/model-weight-management/weight-transformations/weight-persistence.md) — Explains how to serialize trained model parameters to disk for persistence and future restoration.
- [Image Annotation Workflow](https://awesome-repositories.com/f/artificial-intelligence-ml/training-dataset-preparation/image-segmentation-dataset-pipelines/image-annotation-workflow.md) — Provides instructions on labeling images to produce annotated datasets for object detection models. ([source](https://www.bilibili.com/video/BV19Z31z8ENH/))
- [Training Dataset Processing](https://awesome-repositories.com/f/artificial-intelligence-ml/training-dataset-processing.md) — Implements pipelines for batching and processing large-scale datasets for efficient model training. ([source](https://www.bilibili.com/video/av74281036))

### Education & Learning Resources

- [Deep Learning Education](https://awesome-repositories.com/f/education-learning-resources/deep-learning-education.md) — Offers a structured educational resource for learning the fundamental theory and practice of deep learning. ([source](https://www.bilibili.com/video/av74281036/))
- [Computer Vision Tutorials](https://awesome-repositories.com/f/education-learning-resources/computer-vision-tutorials.md) — Provides practical tutorials for implementing image classification and object detection using convolutional neural networks.
- [Deep Learning Courses](https://awesome-repositories.com/f/education-learning-resources/deep-learning-courses.md) — Provides a structured curriculum for designing, training, and validating deep learning models.
- [Deep Learning Fundamentals](https://awesome-repositories.com/f/education-learning-resources/deep-learning-curriculum/deep-learning-fundamentals.md) — Provides foundational educational content on tensors and neural network layers for beginners.
- [PyTorch Deep Learning Examples](https://awesome-repositories.com/f/education-learning-resources/deep-learning-education/deep-learning-platforms/pytorch-deep-learning-examples.md) — Provides beginner-friendly PyTorch examples and step-by-step guides for building and training neural networks.

### Scientific & Mathematical Computing

- [GPU-Accelerated Computation](https://awesome-repositories.com/f/scientific-mathematical-computing/gpu-accelerated-computation.md) — Provides instructions for offloading heavy mathematical computations to GPUs to accelerate training and inference.
- [Tensor Operations](https://awesome-repositories.com/f/scientific-mathematical-computing/tensor-operations.md) — Covers fundamental tensor operations and multi-dimensional array manipulations for efficient computation.
- [Linear Algebra Routines](https://awesome-repositories.com/f/scientific-mathematical-computing/linear-algebra-routines.md) — Provides examples of high-performance matrix multiplications and linear algebra transformations using tensors.

### Data & Databases

- [Data Loading Pipelines](https://awesome-repositories.com/f/data-databases/data-pipeline-orchestration/data-engineering-pipelines/batched-data-loading/data-loading-pipelines.md) — Implements data loading pipelines that prepare and transform raw datasets for batch processing.