Sebastian Raschka Building LLMs from the Ground Up: A 3-hour Coding Workshop [https://www.youtube.com/watch?v=quh7z1q7-uc&list=PLTKMiZHVd_2Licpov-ZK24j6oUnbhiPkm&index=5&t=6464s](https://www.youtube.com/watch?v=quh7z1q7-uc&list=PLTKMiZHVd_2Licpov-ZK24j6oUnbhiPkm&index=5&t=6464s) [LLM Resource Hub](https://llmresourceshub.vercel.app/) Lora quantization MoE GRPO https://x.com/maximelabonne/status/1896594006324244680?t=qh2rUwwSptIxzE50HytmMQ&s=19 https://x.com/Hesamation/status/1896675885572554798?t=RtfDq8tFM2Qv_ni6a9SHfQ&s=19 pytorch distributed training Deboilerplating pytorch [Welcome to ⚡ PyTorch Lightning — PyTorch Lightning 2.5.1.post0 documentation](https://lightning.ai/docs/pytorch/stable/) AI agents Berkeley course [https://www.youtube.com/watch?v=g0Dwtf3BH-0&list=PLS01nW3RtgorL3AW8REU9nGkzhvtn6Egn&index=5](https://www.youtube.com/watch?v=g0Dwtf3BH-0&list=PLS01nW3RtgorL3AW8REU9nGkzhvtn6Egn&index=5) XGBoost from scratch [Formulating and Implementing XGBoost From Scratch](https://www.dailydoseofds.com/formulating-and-implementing-xgboost-from-scratch/) [Supervised Machine Learning for Science](https://ml-science-book.com/) [Hands-On Large Language Models](https://www.llm-book.com/) [MLOps guide](https://huyenchip.com/mlops/) Hands-On Generative AI with Transformers and Diffusion Models [Hands-On Generative AI with Transformers and Diffusion Models[Book]](https://www.oreilly.com/library/view/hands-on-generative-ai/9781098149239/) https://x.com/osanseviero/status/1868270360560369884 ai engineering book [GitHub - chiphuyen/aie-book: [WIP] Resources for AI engineers. Also contains supporting materials for the book AI Engineering (Chip Huyen, 2025)](https://github.com/chiphuyen/aie-book/tree/main) AI for medicine [AI for Medicine | Coursera](https://www.coursera.org/specializations/ai-for-medicine) [10 GitHub Repositories for Deep Learning Enthusiasts - KDnuggets](https://www.kdnuggets.com/10-github-repositories-for-deep-learning-enthusiasts) Free paid courses torrents https://x.com/InterestingSTEM/status/1825118300788994261?t=xxg5aeTvD58YkroES2pHnA&s=19 Leet code Information theory oxford [https://www.youtube.com/watch?v=ScX2aBFyrVU](https://www.youtube.com/watch?v=ScX2aBFyrVU) [Reddit - The heart of the internet](https://www.reddit.com/r/deeplearning/comments/1eyblql/deep_learning_roadmap_with_free_resources/) Stanford CS224N: NLP with Deep Learning [https://www.youtube.com/watch?v=DzpHeXVSC5I](https://www.youtube.com/watch?v=DzpHeXVSC5I) Ml leet code Visual superposition [https://youtu.be/qGQ5U3dkZzk?si=5XLn-eXEtbWjouzR](https://youtu.be/qGQ5U3dkZzk?si=5XLn-eXEtbWjouzR) Probabilistic Programming course [Než budete pokračovat na YouTube](https://www.youtube.com/playlist?list=PLRBUAK6di_6XlF7KAZBPRgcP0zD5sVXcN) Llmops [GitHub - decodingml/llm-twin-course: 🤖 𝗟𝗲𝗮𝗿𝗻 for 𝗳𝗿𝗲𝗲 how to 𝗯𝘂𝗶𝗹𝗱 an end-to-end 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗿𝗲𝗮𝗱𝘆 𝗟𝗟𝗠 & 𝗥𝗔𝗚 𝘀𝘆𝘀𝘁𝗲𝗺 using 𝗟𝗟𝗠𝗢𝗽𝘀 best practices: ~ 𝘴𝘰𝘶𝘳𝘤𝘦 𝘤𝘰𝘥𝘦 + 12 𝘩𝘢𝘯𝘥𝘴-𝘰𝘯 𝘭𝘦𝘴𝘴𝘰𝘯𝘴](https://github.com/decodingml/llm-twin-course) Pytorch in day [https://youtu.be/Z_ikDlimN6A?si=_Cmbtk-Iopdk-1kH](https://youtu.be/Z_ikDlimN6A?si=_Cmbtk-Iopdk-1kH) Statistics tests https://x.com/acagamic/status/1824113869796811047?t=6uG3K4t_2kUysGbTyHGEPw&s=19 coding ai paper [Než budete pokračovat na YouTube](https://www.youtube.com/playlist?list=PLam9sigHPGwOe8VDoS_6VT4jjlgs9Uepb) [GitHub - rasbt/LLMs-from-scratch: Implement a ChatGPT-like LLM in PyTorch from scratch, step by step](https://github.com/rasbt/LLMs-from-scratch) [Pretraining LLMs - DeepLearning.AI](https://www.deeplearning.ai/short-courses/pretraining-llms/) [DeepLearning.AI Data Engineering Professional Certificate | Coursera](https://www.coursera.org/professional-certificates/data-engineering) PCA, t-SNE, UMAP [https://www.youtube.com/watch?v=o_cAOa5fMhE](https://www.youtube.com/watch?v=o_cAOa5fMhE) Boltzmann Machines [https://www.youtube.com/watch?v=_bqa_I5hNAo](https://www.youtube.com/watch?v=_bqa_I5hNAo) [Machine Learning with PyTorch and Scikit-Learn[Book]](https://www.oreilly.com/library/view/machine-learning-with/9781801819312/) [GitHub - rasbt/machine-learning-book: Code Repository for Machine Learning with PyTorch and Scikit-Learn](https://github.com/rasbt/machine-learning-book) [GitHub - EleutherAI/cookbook: Deep learning for dummies. All the practical details and useful utilities that go into working with real models.](https://github.com/EleutherAI/cookbook) [Hands-on Machine Learning with Python: Implement Neural Network Solutions with Scikit-learn and PyTorch[Book]](https://www.oreilly.com/library/view/hands-on-machine-learning/9781484279212/) [An Open Course on LLMs, Led by Practitioners – Hamel’s Blog](https://hamel.dev/blog/posts/course/) LLM course by practicioners Give directly learn data engineering [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PLxy0DxWEupiNjGSv1hzRFBXgSzV-bZu94&si=DeB5Ncd44fFNJBMU) [https://youtu.be/BlWS4foN9cY?si=3pp_1RuI1_fbIfix](https://youtu.be/BlWS4foN9cY?si=3pp_1RuI1_fbIfix) understanding deep learning [Understanding Deep Learning](https://udlbook.github.io/udlbook/) https://x.com/maximelabonne/status/1813510236176601204?t=w85b2KpUXcwTqal1L9S5XQ&s=19 Cornell applied machine learning lectures code https://x.com/Jeande_d/status/1855960964446802070?t=nAdl-OVlk9zSe6fGX-L1oA&s=19 Rlhf from scratch [https://youtu.be/aI8cyr-gH6M?si=lkmx9M9_v4WgdBnO](https://youtu.be/aI8cyr-gH6M?si=lkmx9M9_v4WgdBnO) [GitHub - SylphAI-Inc/LLM-engineer-handbook: A curated list of Large Language Model resources, covering model training, serving, fine-tuning, and building LLM applications.](https://github.com/SylphAI-Inc/llm-engineer-handbook) stanford reinforcement learning [Než budete pokračovat na YouTube](https://www.youtube.com/playlist?list=PLoROMvodv4rN4wG6Nk6sNpTEbuOSosZdX) Building LLMS From Scratch by Sebastian Raschka [https://www.youtube.com/watch?v=kPGTx4wcm_w](https://www.youtube.com/watch?v=kPGTx4wcm_w) Stockfish [GitHub - PacktPublishing/LLM-Engineers-Handbook: The LLM's practical guide: From the fundamentals to deploying advanced LLM and RAG apps to AWS using LLMOps best practices](https://github.com/PacktPublishing/LLM-Engineers-Handbook) statsitics [https://www.youtube.com/watch?v=Ym1iH8-GQOE&list=PLmQOGaOyjl0uMOkQxY81XxN8p2yUCICJ_&index=26&t=5463s](https://www.youtube.com/watch?v=Ym1iH8-GQOE&list=PLmQOGaOyjl0uMOkQxY81XxN8p2yUCICJ_&index=26&t=5463s) biostatistics [https://www.youtube.com/watch?v=1Q6_LRZwZrc&list=PLmQOGaOyjl0uMOkQxY81XxN8p2yUCICJ_&index=27](https://www.youtube.com/watch?v=1Q6_LRZwZrc&list=PLmQOGaOyjl0uMOkQxY81XxN8p2yUCICJ_&index=27) Read papers mentioned by Karpathy Financial machine learning [GitHub - firmai/financial-machine-learning: A curated list of practical financial machine learning tools and applications.](https://github.com/firmai/financial-machine-learning) Vision transformers Variational autoencoders Mutual information reinforcement learning AI theory [Reddit - The heart of the internet](https://www.reddit.com/r/learnmachinelearning/s/BzxgxxrsF5) Rlhf [https://www.youtube.com/watch?v=BqZC7mDSbIg](https://www.youtube.com/watch?v=BqZC7mDSbIg) [https://www.youtube.com/watch?v=XZLc09hkMwA](https://www.youtube.com/watch?v=XZLc09hkMwA) Constituonal AI Zeroth-01 Bot: the world's smallest open-source end-to-end humanoid robot starting at $350! https://x.com/JingxiangMo/status/1856148967819751817?t=mTa-iBpJLxHcmUYYej2Z6Q&s=19 Weak to strong generalization [Build a Large Language Model (From Scratch)](https://www.manning.com/books/build-a-large-language-model-from-scratch) Xdboost Technical LLM engineering books https://www.linkedin.com/posts/udaykamath_if-you-go-to-amazon-you-will-see-these-are-activity-7261047054104195072-I8S7?utm_source=share&utm_medium=member_android Diffusion and autoregressive vision transformers I'm noticing people merging these recently like multimodal diffusion transformer And there's flow matching Approximating differentiable curvefitted solution approximating all functions using grokked fourier series algorithm? Fourier series approximating any differential curvefitted solution? Isomorphism? Taylor series approximations? Spline interpolation? Gaussian mixture models? Support vector machines? Decision trees? Random forests? Wavelets? General universal approximators of arbitrary functions? Generalized approximation theorem? Space of all possible general universal approximators? ML books [https://franknielsen.github.io/Books/CuratedBookLists.html](https://franknielsen.github.io/Books/CuratedBookLists.html) AI Webinars List (LLMs/RAG/Generative AI/ ML/Vector Database) https://www.marktechpost.com/ai-webinars-list-llms-rag-generative-ai-ml-vector-database/ Quantum machine learning [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PLOFEBzvs-VvqJwybFxkTiDzhf5E11p8BI&si=ku2jEARSlICNmniy) [Než budete pokračovat na YouTube](https://www.youtube.com/live/lkmehsypdag?si=LPB74Ir_-eUQshaK) data engineering [GitHub - DataExpert-io/data-engineer-handbook: This is a repo with links to everything you'd ever want to learn about data engineering](https://github.com/DataExpert-io/data-engineer-handbook) [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PLxy0DxWEupiNjGSv1hzRFBXgSzV-bZu94&si=DeB5Ncd44fFNJBMU) [https://youtu.be/BlWS4foN9cY?si=3pp_1RuI1_fbIfix](https://youtu.be/BlWS4foN9cY?si=3pp_1RuI1_fbIfix) Neural Turing machines Neural cellular automata spiking neural networks Where to learn ML https://x.com/thenaijacarguy/status/1855939707370168712?t=o8VA2wOD8t4wcK7CvEruVg&s=19 amortized bayesian inference AI big data MIT [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PLUl4u3cNGP62uI_DWNdWoIMsgPcLGOx-V&si=3q_CmQJTyVJgnXxT) Theory pac learning VC dimension [Understanding Deep Learning](https://udlbook.github.io/udlbook/) understanding deep learning book [ML Resources](https://ml-resources.vercel.app/) Reinforcement learning overview [[2412.05265] Reinforcement Learning: An Overview](https://arxiv.org/abs/2412.05265) markov chain monte carlo [https://www.youtube.com/watch?v=Jr1GdNI3Vfo](https://www.youtube.com/watch?v=Jr1GdNI3Vfo) Subbarao Kambhampati AI agents lectures [https://www.youtube.com/watch?v=HNuORbjUg_s&list=PLNONVE5W8PCTuEA0IbtXYGyTNWld5fyuD](https://www.youtube.com/watch?v=HNuORbjUg_s&list=PLNONVE5W8PCTuEA0IbtXYGyTNWld5fyuD) information theory harvard [Než budete pokračovat na YouTube](https://www.youtube.com/playlist?list=PLDEN2FPNHwVZKAFqfFl1b_NNAESTJwV9o) [https://www.youtube.com/results?search_query=information+theory&sp=EgIQAw%253D%253D](https://www.youtube.com/results?search_query=information+theory&sp=EgIQAw%253D%253D) MIT probability [https://www.youtube.com/watch?v=ZgCBmERwZlI](https://www.youtube.com/watch?v=ZgCBmERwZlI) caltech machine learning lectures [https://www.youtube.com/watch?v=Dc0sr0kdBVI](https://www.youtube.com/watch?v=Dc0sr0kdBVI) Quantum machine learning [Než budete pokračovat na YouTube](https://www.youtube.com/playlist?list=PLOFEBzvs-VvqJwybFxkTiDzhf5E11p8BI) [Než budete pokračovat na YouTube](https://www.youtube.com/playlist?list=PLmRxgFnCIhaMgvot-Xuym_hn69lmzIokg) [Než budete pokračovat na YouTube](https://www.youtube.com/playlist?list=PLHgX2IExbFosaWB4cuGyal0pmS3gsI7je) [GitHub - codecrafters-io/build-your-own-x: Master programming by recreating your favorite technologies from scratch.](https://github.com/codecrafters-io/build-your-own-x) [https://www.youtube.com/watch?v=laaBLUxJUMY](https://www.youtube.com/watch?v=laaBLUxJUMY) Snake deep reinforcement learning [https://www.youtube.com/watch?v=PJl4iabBEz0&list=PLqnslRFeH2UrDh7vUmJ60YrmWd64mTTKV&index=1](https://www.youtube.com/watch?v=PJl4iabBEz0&list=PLqnslRFeH2UrDh7vUmJ60YrmWd64mTTKV&index=1) [GitHub - patrickloeber/snake-ai-pytorch](https://github.com/patrickloeber/snake-ai-pytorch) Natural language processing with deep learning Stanford [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PLoROMvodv4rOaMFbaqxPDoLWjDaRAdP9D&si=4dXon6SW3pPPCmBj) Stanford reinforcement learning [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PLoROMvodv4rN4wG6Nk6sNpTEbuOSosZdX&si=_KfXT1EL2zd0b2FV) AlphaFold 2 from scratch [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PLJ0WcPQS7xJVJr6ceIPFSkAGAgrkmw1c9&si=3OpI9BrAhQ836s6s) Physics informed machine learning neural networks Steve Brunton [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PLMrJAkhIeNNQ0BaKuBKY43k4xMo6NSbBa&si=9PM8PhWpgfe9RdG_)