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The 10 GitHub Repository Education Series has been a hit among readers, so here is another list to help you master the basics of deep learning. This collection will guide you through understanding popular deep learning frameworks and various model architectures. In short, you will learn everything from scratch and gain the skills needed to build your own deep learning models. Whether you are a beginner or looking to deepen your knowledge, these resources will provide a comprehensive foundation in deep learning.
1. Annotated Deep Learning Paper Implementations
This repository offers 60 implementations and tutorials of deep learning papers with side-by-side notes. It includes models like transformers, GANs, and reinforcement learning algorithms. The detailed annotations make it an excellent resource for understanding complex papers and their implementations. The most important part of this resource is that with one click, you can launch the code in Google Colab and run the code for free.
Link: labmlai/annotated_deep_learning_paper_implementations
2. Netron: Visualizer for Neural Networks
Netron is a visualizer for neural networks, deep learning, and machine learning models. It supports a wide range of model formats, including ONNX, Pickle, TensorFlow, Keras, and PyTorch. This tool is invaluable for visualizing and debugging models, making it easier to understand their structure and flow. It offers visualization for even simple models like linear regression or random forest classifiers. Netron is a fun tool to have for presentations, project documentation, and learning about the structure of the model.
Link: lutzroeder/netron
3. Fastbook: The FastAI Book
The fastai book, published as Jupyter Notebooks, is a comprehensive introduction to deep learning using the fastai library and PyTorch. These notebooks cover various topics, from basic concepts to advanced techniques, making it a great starting point for anyone looking to dive into deep learning. I have taken the fastai course and followed the fastai book, and I can surely say that if you want to get a start in the field of AI, then you should start taking this course and learn by doing.
Link: fastai/fastbook
4. Awesome Deep Learning
This curated list of awesome deep learning tutorials, projects, and communities is a treasure trove of resources. It includes links to books, courses, videos and lectures, papers, tutorials, research papers, websites, datasets, conferences, frameworks, and tools, providing a broad spectrum of learning materials for deep learning enthusiasts.
Link: ChristosChristofidis/awesome-deep-learning
5. Dive into Deep Learning
Dive into Deep Learning is an interactive deep learning book with multi-framework (PyTorch, NumPy/MXNet, JAX, and TensorFlow) code, math, and discussions. It has been adopted by over 500 universities worldwide, including prestigious institutions like Stanford and MIT. The book is available online and provides a structured approach to learning the basics and advanced topics. Each chapter has code examples, math formulas, a Jupyter Notebook, and implementation in different machine learning frameworks.
Link: d2l-ai/d2l-en
6. Deep Learning with Python Notebooks
The repository contains the Jupyter notebooks implementing the code samples found in the book “Deep Learning with Python” by François Chollet, the creator of Keras. The notebooks provide practical examples and exercises, making understanding and implementing deep learning concepts easier. Each notebook can be opened in the Google Colab and run within a second. The book also provides best practices for tackling real-world machine learning problems.
Link: fchollet/deep-learning-with-python-notebooks
7. Deep Learning Models Collection
This repository is a collection of various deep learning architectures, models, and tips. It includes implementations of popular models using Tensorflow, Pytroch, and Pythoch lighting. The repository provides insights into models’ inner workings, making it a valuable resource for anyone looking to explore different deep-learning architectures and build upon state-of-the-art solutions.
Link: rasbt/deeplearning-models
8. Machine Learning Tutorials
This repository offers a wide range of machine learning and deep learning tutorials, articles, and other resources. It covers various topics like Feed Forward Networks, Recurrent Neural Nets, LSTM, GRU, Restricted Boltzmann Machine, DBNs, Autoencoders, Convolutional Neural Nets, and Graph Representation Learning. The repository provides links to external resources, making it a comprehensive guide for both beginners and advanced learners.
Link: ujjwalkarn/Machine-Learning-Tutorials
9. NVIDIA Deep Learning Examples
NVIDIA’s Deep Learning Examples repository contains state-of-the-art deep learning scripts organized by models. These scripts are designed to be easy to train and deploy, with reproducible accuracy and performance on enterprise-grade infrastructure. This repository is ideal for deploying high-performance deep learning models on Nvidia GPUs.
Link: NVIDIA/DeepLearningExamples
10. Learn PyTorch for Deep Learning
This repository contains materials for the “Learn PyTorch for Deep Learning: Zero to Mastery” course. It includes notebooks, code examples, and exercises that guide learners from the basics of PyTorch to advanced deep learning techniques. The repository consists of links to the online book version, the first five sections on YouTube, and the GitHub Discussions page. It is regularly updated, meaning that you can access the latest knowledge on deep learning and PyTorch.
Link: mrdbourke/pytorch-deep-learning
Final Thoughts
These 10 GitHub repositories offer a wealth of knowledge and practical tools for anyone interested in deep learning. Even if you are new to data science, you can start learning about deep learning by exploring free courses, books, tools, and other resources available on GitHub repositories. All you need is the determination to become a professional machine learning engineer and get hired to work on the latest AI technologies, such as large language models (LLMs).
Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master’s degree in technology management and a bachelor’s degree in telecommunication engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.
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