Hey everyone!
This repository contains a structured collection of resources that helped me get started with deep learning, particularly in my research projects.
If you are beginning your journey in this domain, I hope you find these materials equally helpful and insightful :)
Feel free to share or suggest additional high-quality resources that could benefit others as well.
P.S. I am still updating this repository whenever I find time :p
- Neural networks (https://www.youtube.com/watch?v=aircAruvnKk&list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi) by 3Blue1Brown
- Neural Networks: Zero to Hero (https://www.youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ) by Andrej Karpathy
- Lecture Collection | Convolutional Neural Networks for for Visual Recognition (Spring 2017) (https://www.youtube.com/playlist?list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv)
- Machine Learning Specialization by Andrew Ng (https://www.youtube.com/playlist?list=PLkDaE6sCZn6FNC6YRfRQc_FbeQrF8BwGI)
- Convolutional Neural Networks (Course 4 of the Deep Learning Specialization) (https://www.youtube.com/watch?v=ArPaAX_PhIs&list=PLkDaE6sCZn6Gl29AoE31iwdVwSG-KnDzF)
- Videos about machine learning research papers (https://www.youtube.com/@YannicKilcher/videos) by Yannic Kilcher
- Swin Transformer (https://www.youtube.com/playlist?list=PL9iXGo3xD8jokWaLB8ZHUkjjv5Y_vPQnZ)
- What are GANs (Generative Adversarial Networks)? (https://www.youtube.com/watch?v=TpMIssRdhco) by IBM Technology
- Deconvolution and Checkerboard Artifacts (https://distill.pub/2016/deconv-checkerboard/)
- Yes you should understand backprop (https://karpathy.medium.com/yes-you-should-understand-backprop-e2f06eab496b)
- Up-sampling with Transposed Convolution (https://naokishibuya.medium.com/up-sampling-with-transposed-convolution-9ae4f2df52d0)
- Zero to Mamba: An intuitive explanation to the Mamba Architecture (https://medium.com/@aiclub.iitm/zero-to-mamba-an-intuitive-explanation-to-the-mamba-architecture-d52265b771ab)
- Mamba Explained (https://thegradient.pub/mamba-explained/)
- A Visual Guide to Mamba and State Space Models (https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-mamba-and-state)
- MAMBA and State Space Models Explained (https://athekunal.medium.com/mamba-and-state-space-models-explained-b1bf3cb3bb77)
- What is a Mamba model? (https://www.ibm.com/think/topics/mamba-model)
- A Recipe for Training Neural Networks (https://karpathy.github.io/2019/04/25/recipe/)
- A Guide to Hand-Calculating FLOPs and MACs (https://medium.com/@pashashaik/a-guide-to-hand-calculating-flops-and-macs-fa5221ce5ccc)
- PyTorch 101: A Practical Guide to Using Hooks (https://medium.com/@heyamit10/pytorch-101-a-practical-guide-to-using-hooks-d64b625f0cc7)
- MONAI is a PyTorch-based, open-source framework for deep learning in healthcare imaging, part of the PyTorch Ecosystem (https://github.com/Project-MONAI/MONAI)
- U-Net: Convolutional Networks for Biomedical Image Segmentation (https://arxiv.org/pdf/1505.04597)
- Transformers for 3D Medical Image Segmentation (https://arxiv.org/pdf/2103.10504)
- Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images (https://arxiv.org/pdf/2201.01266)
- Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining (https://arxiv.org/pdf/2402.03302)
- nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation (https://arxiv.org/pdf/1809.10486)
- Attention Is All You Need (https://arxiv.org/pdf/1706.03762)
- Mamba: Linear-Time Sequence Modeling with Selective State Spaces (https://arxiv.org/pdf/2312.00752)
- Generative Adversarial Nets (https://arxiv.org/pdf/1406.2661)
- ADAM: A METHOD FOR STOCHASTIC OPTIMIZATION (https://arxiv.org/pdf/1412.6980)
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (https://arxiv.org/pdf/2010.11929)
- Swin Transformer: Hierarchical Vision Transformer using Shifted Windows (https://arxiv.org/pdf/2103.14030)
- Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model (https://arxiv.org/pdf/2401.09417)
- MambaVision: A Hybrid Mamba-Transformer Vision Backbone (https://arxiv.org/pdf/2407.08083)
- Learning Transferable Visual Models From Natural Language Supervision (https://arxiv.org/pdf/2103.00020)
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DeepMind x UCL | Introduction to Reinforcement Learning 2015 (2015) (https://www.youtube.com/watch?v=2pWv7GOvuf0&list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ)
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Reinforcement Learning - Developing Intelligent Agents (https://www.youtube.com/playlist?list=PLZbbT5o_s2xoWNVdDudn51XM8lOuZ_Njv)
- Hugging Face Deep Reinforcement Learning Course (https://huggingface.co/learn/deep-rl-course/en/unit0/introduction)
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Reinforcement Learning: An Introduction (https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf)
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Algorithms for Reinforcement Learning (https://sites.ualberta.ca/~szepesva/papers/RLAlgsInMDPs.pdf)
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Deep Q-Learning – Build, Train, and Visualize with PyTorch, Gymnasium, and SB3 (https://www.reinforcementlearningpath.com/deep-q-learning-explained-a-step-by-step-guide-to-build-train-and-visualize-your-first-dqn-agent-with-pytorch-gymnasium-and-stable-baselines3)
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Deep Reinforcement Learning: 0 to 100 (https://towardsdatascience.com/deep-reinforcement-learning-for-dummies/)
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Deep Learning (https://www.deeplearningbook.org/)
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Deep Reinforcement Learning: Pong from Pixels (https://karpathy.github.io/2016/05/31/rl/)
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Welcome to Spinning Up in Deep RL! (https://spinningup.openai.com/en/latest/)
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How to get started with Reinforcement Learning (RL) (https://gordicaleksa.medium.com/how-to-get-started-with-reinforcement-learning-rl-4922fafeaf8c)
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Mathematics for Machine Learning (https://mml-book.github.io/)