# atcold/nyu-dlsp20

**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/atcold-nyu-dlsp20).**

6,809 stars · 2,232 forks · Jupyter Notebook · NOASSERTION

## Links

- GitHub: https://github.com/Atcold/NYU-DLSP20
- Homepage: https://atcold.github.io/NYU-DLSP20/
- awesome-repositories: https://awesome-repositories.com/repository/atcold-nyu-dlsp20.md

## Topics

`deep-learning` `jupyter-notebook` `neural-nets` `pytorch`

## Description

NYU-DLSP20 is a self-paced deep learning course repository that provides a complete educational curriculum covering supervised and unsupervised deep learning fundamentals. The course materials include lecture slides, Jupyter notebooks, and YouTube video recordings, all organized around PyTorch-based code exercises and neural network architecture tutorials.

The course is structured as a sequential progression from fundamentals to advanced architectures, with each lecture building on previous material. Assignments are distributed as Jupyter notebooks that students complete and submit, ensuring a consistent execution environment. Lecture slides and Jupyter notebooks are version-controlled together so each notebook corresponds exactly to a specific lecture session, with code examples embedded directly into slides for live execution during presentations.

The curriculum explores convolutional, autoencoder, generative adversarial, and recurrent network architectures through both theory and practical implementations. Hands-on exercises use PyTorch tensors, autograd, and neural network modules as the primary teaching tool for deep learning concepts, with applications to vision, language, and speech. All course materials are stored in a single GitHub repository for version control and easy distribution, with lectures recorded and distributed as YouTube videos for asynchronous, self-paced access.

## Tags

### Education & Learning Resources

- [Deep Learning Fundamentals](https://awesome-repositories.com/f/education-learning-resources/deep-learning-curriculum/deep-learning-fundamentals.md) — Teaches supervised and unsupervised deep learning fundamentals with applications to vision, language, and speech. ([source](https://cdn.jsdelivr.net/gh/atcold/nyu-dlsp20@master/README.md))
- [Course Repositories](https://awesome-repositories.com/f/education-learning-resources/course-repositories.md) — Provides a complete deep learning course repository with slides, notebooks, and assignments.
- [Deep Learning Courses](https://awesome-repositories.com/f/education-learning-resources/deep-learning-courses.md) — Provides lecture slides, Jupyter notebooks, and YouTube videos covering deep learning fundamentals.
- [Sequential Curricula](https://awesome-repositories.com/f/education-learning-resources/deep-learning-education/advanced-research-topics/sequential-curricula.md) — Organizes lectures in a linear sequence from fundamentals to advanced architectures.
- [PyTorch Code Exercises](https://awesome-repositories.com/f/education-learning-resources/deep-learning-education/deep-learning-platforms/pytorch-deep-learning-examples/pytorch-code-exercises.md) — Ships hands-on PyTorch exercises for practicing tensors, autograd, and neural network training.
- [Video Lectures](https://awesome-repositories.com/f/education-learning-resources/educational-resources/reference-and-media/tutorials-media-curated-lists/interactive-learning-media/video-learning-channels/video-lectures.md) — Records and distributes lectures as YouTube videos for asynchronous, self-paced access.
- [Jupyter Notebook Curricula](https://awesome-repositories.com/f/education-learning-resources/jupyter-notebook-curricula.md) — Delivers assignments as Jupyter notebooks with embedded exercises for consistent execution.
- [Lecture-Notebook Pairs](https://awesome-repositories.com/f/education-learning-resources/jupyter-notebook-curricula/lecture-notebook-pairs.md) — Pairs each lecture with a version-controlled Jupyter notebook containing theory and exercises.
- [Neural Network Tutorials](https://awesome-repositories.com/f/education-learning-resources/neural-network-tutorials.md) — Provides tutorials on convolutional, autoencoder, GAN, and recurrent network architectures.
- [Embedded Notebook Exercises](https://awesome-repositories.com/f/education-learning-resources/coding-exercises/embedded-notebook-exercises.md) — Includes practice exercises embedded in notebooks for building and training neural network models. ([source](https://atcold.github.io/NYU-DLSP20/))

### Artificial Intelligence & ML

- [Neural Network Architectures](https://awesome-repositories.com/f/artificial-intelligence-ml/neural-network-architectures.md) — Explores convolutional, autoencoder, GAN, and recurrent network architectures through theory and practice. ([source](https://cdn.jsdelivr.net/gh/atcold/nyu-dlsp20@master/README.md))

### Part of an Awesome List

- [Deep Learning Lectures](https://awesome-repositories.com/f/awesome-lists/ai/deep-learning-lectures.md) — Delivers self-paced lecture materials combining slides, code examples, and video recordings.

### Scientific & Mathematical Computing

- [Educational Code Notebooks](https://awesome-repositories.com/f/scientific-mathematical-computing/research-analysis-workflows/educational-code-notebooks.md) — Ships Jupyter notebooks with PyTorch code for hands-on practice with tensors and neural networks.

### User Interface & Experience

- [Live Code Execution](https://awesome-repositories.com/f/user-interface-experience/presentation-frameworks/code-presentation-utilities/live-code-execution.md) — Embeds live code snippets in slides for real-time execution during presentations.