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 Stanford CS336 Language Modeling from Scratch https://www.youtube.com/watch?v=SQ3fZ1sAqXI&list=PLoROMvodv4rOY23Y0BoGoBGgQ1zmU_MT_&index=1 Reinforcement learning https://huggingface.co/learn/deep-rl-course/unit0/introduction Huggingface ai courses https://huggingface.co/learn https://x.com/heyshrutimishra/status/1959571462945489322?t=7j14HIXu26P0TmcgdeO_PA&s=19 https://huggingface.co/learn/llm-course/ https://llmresourceshub.vercel.app/ Stanford deep learning https://www.youtube.com/playlist?list=PLh8q7LanNXQz_PDgFypXIRoL0a_YB6efJ 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 LLM everything https://huggingfacetb-smol-training-playbook.hf.space/the-smol-training-playbook-the-secrets-to-building-world-class-llms.pdf Deboilerplating pytorch https://lightning.ai/docs/pytorch/stable/ AI agents Berkeley course https://www.youtube.com/watch?v=g0Dwtf3BH-0&list=PLS01nW3RtgorL3AW8REU9nGkzhvtn6Egn&index=5 XGBoost from scratch https://www.dailydoseofds.com/formulating-and-implementing-xgboost-from-scratch/ https://ml-science-book.com/ https://www.llm-book.com/ https://huyenchip.com/mlops/ Hands-On Generative AI with Transformers and Diffusion Models https://www.oreilly.com/library/view/hands-on-generative-ai/9781098149239/ https://x.com/osanseviero/status/1868270360560369884 ai engineering book https://github.com/chiphuyen/aie-book/tree/main AI for medicine https://www.coursera.org/specializations/ai-for-medicine 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.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 Ml leet code Visual superposition https://youtu.be/qGQ5U3dkZzk?si=5XLn-eXEtbWjouzR Probabilistic Programming course https://www.youtube.com/playlist?list=PLRBUAK6di_6XlF7KAZBPRgcP0zD5sVXcN Llmops https://github.com/decodingml/llm-twin-course Pytorch in day 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 https://www.youtube.com/playlist?list=PLam9sigHPGwOe8VDoS_6VT4jjlgs9Uepb https://github.com/rasbt/LLMs-from-scratch https://www.deeplearning.ai/short-courses/pretraining-llms/ https://www.coursera.org/professional-certificates/data-engineering PCA, t-SNE, UMAP https://www.youtube.com/watch?v=o_cAOa5fMhE Boltzmann Machines https://www.youtube.com/watch?v=_bqa_I5hNAo https://www.oreilly.com/library/view/machine-learning-with/9781801819312/ https://github.com/rasbt/machine-learning-book https://github.com/EleutherAI/cookbook https://www.oreilly.com/library/view/hands-on-machine-learning/9781484279212/ https://hamel.dev/blog/posts/course/ LLM course by practicioners Give directly learn data engineering https://youtube.com/playlist?list=PLxy0DxWEupiNjGSv1hzRFBXgSzV-bZu94&si=DeB5Ncd44fFNJBMU https://youtu.be/BlWS4foN9cY?si=3pp_1RuI1_fbIfix 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://github.com/SylphAI-Inc/llm-engineer-handbook stanford reinforcement learning https://www.youtube.com/playlist?list=PLoROMvodv4rN4wG6Nk6sNpTEbuOSosZdX Building LLMS From Scratch by Sebastian Raschka https://www.youtube.com/watch?v=kPGTx4wcm_w Stockfish https://github.com/PacktPublishing/LLM-Engineers-Handbook statsitics 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 Read papers mentioned by Karpathy Financial machine learning https://github.com/firmai/financial-machine-learning Vision transformers Variational autoencoders Mutual information reinforcement learning AI theory https://www.reddit.com/r/learnmachinelearning/s/BzxgxxrsF5 Rlhf https://www.youtube.com/watch?v=BqZC7mDSbIg 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 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 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 https://youtube.com/playlist?list=PLOFEBzvs-VvqJwybFxkTiDzhf5E11p8BI&si=ku2jEARSlICNmniy https://www.youtube.com/live/lkmehsypdag?si=LPB74Ir_-eUQshaK data engineering https://github.com/DataExpert-io/data-engineer-handbook https://youtube.com/playlist?list=PLxy0DxWEupiNjGSv1hzRFBXgSzV-bZu94&si=DeB5Ncd44fFNJBMU 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 https://youtube.com/playlist?list=PLUl4u3cNGP62uI_DWNdWoIMsgPcLGOx-V&si=3q_CmQJTyVJgnXxT Theory pac learning VC dimension https://udlbook.github.io/udlbook/ understanding deep learning book https://ml-resources.vercel.app/ Reinforcement learning overview https://arxiv.org/abs/2412.05265 markov chain monte carlo https://www.youtube.com/watch?v=Jr1GdNI3Vfo Subbarao Kambhampati AI agents lectures https://www.youtube.com/watch?v=HNuORbjUg_s&list=PLNONVE5W8PCTuEA0IbtXYGyTNWld5fyuD information theory harvard https://www.youtube.com/playlist?list=PLDEN2FPNHwVZKAFqfFl1b_NNAESTJwV9o https://www.youtube.com/results?search_query=information+theory&sp=EgIQAw%253D%253D MIT probability https://www.youtube.com/watch?v=ZgCBmERwZlI caltech machine learning lectures https://www.youtube.com/watch?v=Dc0sr0kdBVI Quantum machine learning https://www.youtube.com/playlist?list=PLOFEBzvs-VvqJwybFxkTiDzhf5E11p8BI https://www.youtube.com/playlist?list=PLmRxgFnCIhaMgvot-Xuym_hn69lmzIokg https://www.youtube.com/playlist?list=PLHgX2IExbFosaWB4cuGyal0pmS3gsI7je https://github.com/codecrafters-io/build-your-own-x https://www.youtube.com/watch?v=laaBLUxJUMY Snake deep reinforcement learning https://www.youtube.com/watch?v=PJl4iabBEz0&list=PLqnslRFeH2UrDh7vUmJ60YrmWd64mTTKV&index=1 https://github.com/patrickloeber/snake-ai-pytorch Natural language processing with deep learning Stanford https://youtube.com/playlist?list=PLoROMvodv4rOaMFbaqxPDoLWjDaRAdP9D&si=4dXon6SW3pPPCmBj Stanford reinforcement learning https://youtube.com/playlist?list=PLoROMvodv4rN4wG6Nk6sNpTEbuOSosZdX&si=_KfXT1EL2zd0b2FV AlphaFold 2 from scratch https://youtube.com/playlist?list=PLJ0WcPQS7xJVJr6ceIPFSkAGAgrkmw1c9&si=3OpI9BrAhQ836s6s Physics informed machine learning neural networks Steve Brunton https://youtube.com/playlist?list=PLMrJAkhIeNNQ0BaKuBKY43k4xMo6NSbBa&si=9PM8PhWpgfe9RdG_ Stanford CME295: Transformers and Large Language Models https://www.youtube.com/playlist?list=PLoROMvodv4rOCXd21gf0CF4xr35yINeOy https://arxiv.org/abs/2603.18387 MIT deep learning https://www.youtube.com/playlist?list=PLUl4u3cNGP63URZnh5iqBzDTDYPUTQT-8 https://tinker-docs.thinkingmachines.ai/tutorials/ I'm looking around and trying to figure out which is the best book to recommend to people who want to learn ML/AI by doing (with exercises where you train models on datasets) but that also has some math in it. This one seems like the best fit for that. Would you recommend any others? https://www.oreilly.com/library/view/hands-on-machine-learning/9798341607972/ - Hands-On Machine Learning with Scikit-Learn and PyTorch very pracical everything and also mathy and also includes other ml methods - https://deeplearningwithpython.io/ - no excercises, chollet - https://udlbook.github.io/udlbook/ - mathy excercises - https://d2l.ai/ - worse excercises than Hands-On Machine Learning with Scikit-Learn and PyTorch ? also mathy - Deep Learning with PyTorch, Second Edition - very practical! - fast ai not enough math? kinda missing open ended excercises - CS231n's excercises are mostly implement algorithm from scratch or solve math - https://www.bishopbook.com/ mathy excercises - Machine Learning with PyTorch and Scikit-Learn unclear and scattered excercises - Learning Theory from First Principles Francis Bach just math - mit https://introtodeeplearning.com/ with excercises in code but not much diverse and openended tutorials on pytroch, skicit learn, keras, huggingface libraries, etc. There are also many interesting websites about machine learning, including Scikit-Learn’s exceptional User Guide. You may also enjoy Dataquest, which provides very nice interactive tutorials, and countless ML blogs and YouTube channels. There are many other introductory books about machine learning. In particular: Joel Grus’s Data Science from Scratch, 2nd edition (O’Reilly), presents the fundamentals of machine learning and implements some of the main algorithms in pure Python (from scratch, as the name suggests). Stephen Marsland’s Machine Learning: An Algorithmic Perspective, 2nd edition (Chapman & Hall), is a great introduction to machine learning, covering a wide range of topics in depth with code examples in Python (also from scratch, but using NumPy). Sebastian Raschka’s Machine Learning with PyTorch and Scikit-Learn, 1st edition (Packt Publishing), is also a great introduction to machine learning using Scikit-Learn and PyTorch. François Chollet’s Deep Learning with Python, 3rd edition (Manning), is a very practical book that covers a large range of topics in a clear and concise way, as you might expect from the author of the excellent Keras library. It favors code examples over mathematical theory. Andriy Burkov’s The Hundred-Page Machine Learning Book (self-published) is very short but covers an impressive range of topics, introducing them in approachable terms without shying away from the math equations. Yaser S. Abu-Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin’s Learning from Data (AMLBook) is a more theoretical approach to ML that provides deep insights, in particular on the bias/variance trade-off (see Chapter 4). Stuart Russell and Peter Norvig’s Artificial Intelligence: A Modern Approach, 4th edition (Pearson), is a great (and huge) book covering an incredible amount of topics, including machine learning. It helps put ML into perspective. Jeremy Howard and Sylvain Gugger’s Deep Learning for Coders with fastai and PyTorch (O’Reilly) provides a wonderfully clear and practical introduction to deep learning using the fastai and PyTorch libraries. Andrew Ng’s Machine Learning Yearning is a free ebook that provides a thoughtful exploration of machine learning, focusing on the practical considerations of building and deploying models, including data quality and long-term maintenance. Lewis Tunstall, Leandro von Werra, and Thomas Wolf’s Natural Language Processing with Transformers: Building Language Applications with Hugging Face (O’Reilly) is a great practical dive into transformers using popular libraries by Hugging Face. Jay Alammar and Maarten Grootendorst’s Hands-On Large Language Models is a beautifully illustrated book on LLMs, covering everything you need to know to understand, train, fine-tune, and use LLMs across a wide variety of tasks. Finally, joining ML competition websites such as Kaggle.com will allow you to practice your skills on real-world problems, with help and insights from some of the best ML professionals out there. https://www.reddit.com/r/learnmachinelearning/comments/1tbqnyd/i_want_the_best_basic_machine_learning_book/ Frontier lab resources https://x.com/i/status/2056478391008977404 AI safety: BlueDot AI Safety BlueDot Biosecurity ARENA ML4Good CAIS Intro to ML Safety CAIS AI Safety, Ethics, and Society AI Safety Camp CAIS AI Safety Ethics & Society causality https://youtube.com/playlist?list=PLgKuh-lKre11SiNLE2BNNg59MGcTCpbQx&si=licuyv8fjyeTUFY7