# mrdbourke/pytorch-deep-learning

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17,195 stars · 4,745 forks · Jupyter Notebook · mit

## Links

- GitHub: https://github.com/mrdbourke/pytorch-deep-learning
- Homepage: https://learnpytorch.io
- awesome-repositories: https://awesome-repositories.com/repository/mrdbourke-pytorch-deep-learning.md

## Topics

`deep-learning` `machine-learning` `pytorch`

## Description

This project is a structured educational resource and training platform designed for mastering deep learning development. It provides a comprehensive curriculum focused on building, evaluating, and refining predictive models through hands-on coding exercises and standard industry workflows.

The curriculum emphasizes practical implementation, guiding users through the construction of neural network architectures and the application of transfer learning to adapt pretrained models for custom tasks. It includes methodologies for tracking and comparing model experiment results, allowing for the systematic optimization of training configurations and performance metrics.

The resource covers the end-to-end development lifecycle, ranging from initial model design and iterative training to the deployment of predictive models as functional web applications. It specifically focuses on computer vision tasks, providing a guide for implementing image classification and feature extraction models. The content is delivered through a collection of Jupyter Notebooks that facilitate a hands-on approach to learning deep learning fundamentals.

## Tags

### Artificial Intelligence & ML

- [Guides](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/frameworks/computer-vision/guides.md) — Offers a practical curriculum for implementing and deploying computer vision models.
- [Neural Network Layers](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/frameworks/model-construction/neural-network-layers.md) — Provides a structured workflow for constructing and training neural network models from scratch. ([source](https://learnpytorch.io))
- [Machine Learning Training](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training.md) — Implements transfer learning workflows to refine pretrained models for custom tasks. ([source](https://learnpytorch.io))
- [Model Fine-Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/fine-tuning-and-customization/model-fine-tuning.md) — Adapts pretrained models to custom datasets by fine-tuning classification layers.
- [Transfer Learning](https://awesome-repositories.com/f/artificial-intelligence-ml/transfer-learning.md) — Provides practical implementations for adapting pretrained models to custom tasks.
- [Model Deployment Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-deployment-and-serving/deployment-pipelines-and-endpoints/model-deployment-pipelines.md) — Guides the deployment of trained predictive models as functional web applications. ([source](https://learnpytorch.io))
- [Model Inference and Serving](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-inference-serving.md) — Supports the deployment of predictive models as live web services for end users.
- [Experiment Tracking](https://awesome-repositories.com/f/artificial-intelligence-ml/experiment-tracking.md) — Tracks and compares model experiment results to optimize training configurations. ([source](https://learnpytorch.io))
- [Machine Learning Experiment Trackers](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning-experiment-trackers.md) — Enables systematic logging and comparison of machine learning model iterations.
- [Training Execution Loops](https://awesome-repositories.com/f/artificial-intelligence-ml/neural-training-pipelines/training-execution-loops.md) — Executes iterative training loops to optimize model parameters through forward passes and loss calculation.
- [Training Resources](https://awesome-repositories.com/f/artificial-intelligence-ml/training-resources.md) — Serves as a comprehensive educational resource for learning the technical processes of training and refining machine learning models.
- [Dynamic Graph Builders](https://awesome-repositories.com/f/artificial-intelligence-ml/gradient-computation/dynamic-graph-builders.md) — Supports dynamic computational graph construction to enable flexible neural network training workflows.
- [Sequential Layer Containers](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/frameworks/model-construction/neural-network-layers/normalization-layers/sequential-layer-containers.md) — Uses sequential containers to compose neural network architectures through modular layer stacking.
- [Machine Learning Platforms](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/integrated-development-platforms/machine-learning-platforms.md) — Offers an end-to-end development environment for building, training, and deploying predictive models.

### Education & Learning Resources

- [Deep Learning Curricula](https://awesome-repositories.com/f/education-learning-resources/deep-learning-curricula.md) — Provides a structured, hands-on curriculum for mastering deep learning fundamentals through practical coding exercises and industry-standard workflows.
- [Deep Learning Fundamentals](https://awesome-repositories.com/f/education-learning-resources/deep-learning-curriculum/deep-learning-fundamentals.md) — Teaches core deep learning concepts through a structured, hands-on curriculum. ([source](https://learnpytorch.io))
- [Deep Learning Education](https://awesome-repositories.com/f/education-learning-resources/deep-learning-education.md) — Delivers a structured curriculum for mastering deep learning using the PyTorch framework.
- [Deep Learning Curriculum](https://awesome-repositories.com/f/education-learning-resources/deep-learning-curriculum.md) — Delivers a structured educational path for mastering neural network development and deployment using the PyTorch framework.
- [Deep Learning Platforms](https://awesome-repositories.com/f/education-learning-resources/deep-learning-education/deep-learning-platforms.md) — Facilitates end-to-end deep learning model development using structured workflows.

### Scientific & Mathematical Computing

- [Tensor Computation Graphs](https://awesome-repositories.com/f/scientific-mathematical-computing/high-performance-execution-environments/scientific-computing-platforms/computational-frameworks/tensor-computation-graphs.md) — Provides automated gradient calculation through tensor computation graphs for neural network training.