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AI_Resources
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AI/ML Resources For Beginners
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# AI Resources Some resources to start out with (definitely worth going down other interesting rabbit holes!). --- ## Courses & Guided Tracks - [3B1B Neural Networks intro](https://www.youtube.com/watch?v=aircAruvnKk&list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi) - [Taking Andrew Ng's ML specialization course](https://www.deeplearning.ai/courses/machine-learning-specialization/) --- ## Lectures, Talk Series & YouTube Channels - [Andrej Karpathy — watching many of his videos (includes "GPT from scratch")](https://www.youtube.com/andrejkarpathy) - [Pieter Abbeel lectures](https://www.youtube.com/@PieterAbbeel/playlists) - [MIT AI lectures](https://m.youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI) - [Scaling talk (Stanford Online)](https://www.youtube.com/watch?v=orDKvo8h71o&ab_channel=StanfordOnline) - [Watching some of Yannic Kilcher's videos is also useful](https://www.youtube.com/@YannicKilcher) --- ## How to Read / Paper Lists - [How to read papers (fast.ai forum)](https://forums.fast.ai/t/how-to-read-research-papers-andrew-ng/66892) - [Ilya Sutskever list of papers/topics](https://arc.net/folder/D0472A20-9C20-4D3F-B145-D2865C0A9FEE) - [Reddit thread: "The ML papers that rocked our world 2020–2023"](https://www.reddit.com/r/MachineLearning/comments/16ij18f/d_the_ml_papers_that_rocked_our_world_20202023/) --- ## Core Papers - [ResNet](https://arxiv.org/abs/1512.03385) - [VAEs](https://arxiv.org/abs/1312.6114) - [GANs](https://arxiv.org/pdf/1406.2661) - [Transformers](https://arxiv.org/abs/1706.03762) - [VQ-VAE](https://arxiv.org/abs/1711.00937) - [GPT2](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) - [Vision Transformers](https://arxiv.org/abs/2010.11929) - [Scaling Laws](https://arxiv.org/abs/2001.08361) --- ## Self-Supervised Learning (SSL) - [Contrastive Predictive Coding](https://arxiv.org/pdf/1807.03748) - [CLIP](https://arxiv.org/abs/2103.00020) - [MAE](https://arxiv.org/abs/2111.06377) - [DINO](https://arxiv.org/pdf/2104.14294) - [SSL Cookbook](https://arxiv.org/pdf/2304.12210) - [Yann Lecun's Jepa](https://openreview.net/pdf?id=BZ5a1r-kVsf) --- ## World Models / Planning - [World Models](https://arxiv.org/pdf/1803.10122) - [Dreamerv1](https://arxiv.org/pdf/1912.01603), [Dreamerv3](https://arxiv.org/abs/2301.04104) - [Dino-WM](https://arxiv.org/abs/2411.04983) - [Diffuser](https://arxiv.org/abs/2205.09991) --- ## Diffusion (videos + papers) - [Diffusion explainer — Umar Jamil](https://www.youtube.com/watch?v=ZBKpAp_6TGI&ab_channel=UmarJamil) - [Jeremy Howard talk (part 1)](https://www.youtube.com/watch?v=_7rMfsA24Ls&t=7035s&ab_channel=JeremyHoward) - [Jeremy Howard talk (part 2)](https://www.youtube.com/watch?v=6StU6UtZEbU&t=1194s&ab_channel=JeremyHoward) - [Denoising Diffusion Probabilistic Models](https://arxiv.org/pdf/2006.11239) - [Latent Diffusion (LDMs)](https://arxiv.org/pdf/2112.10752) - [Diffusion Transformers (DiTs)](https://arxiv.org/pdf/2212.09748) - [MIT Diffusion models intro](https://www.youtube.com/watch?v=EUNr6Z_xbCA&list=PL_1TbuIu65A_G908tHHvTnyQsueR17rMh&index=4) - [Flow = Diffusion](https://diffusionflow.github.io/) - [Flow, OT, and rectified flow explained simply](https://alechelbling.com/blog/rectified-flow/) --- ## Energy-Based Models (videos + papers) - [Energy-Based Transformers covered by yannic kilcher](https://www.youtube.com/watch?v=RAEy3JZmIaA) - [Energy-Based Transformers covered by bycloud](https://www.youtube.com/watch?v=LUQkWzjv2RM) - [Energy-Based Transformers covered by AI Coffee Break](https://www.youtube.com/watch?v=18Fn2m99X1k) - [Energy-Based Transformers](https://energy-based-transformers.github.io/), (see sections H and I for an easy to understand intro) - [EBT Blog](https://alexiglad.github.io/blog/2025/ebt/) - [Implicit Generation and Generalization](https://arxiv.org/pdf/1903.08689) - [Iterative reasoning as energy minimization](https://arxiv.org/pdf/2206.15448) - [Iterative reasoning as energy diffusion](https://arxiv.org/pdf/2406.11179) - [EBMs for compositional generalization](https://arxiv.org/abs/2302.11552) --- ## Blogs & Practical Guides - [Karpathy — debugging/training ("Recipe")](https://karpathy.github.io/2019/04/25/recipe/) - [Google Research — hparam tuning playbook](https://github.com/google-research/tuning_playbook#who-is-this-document-for) - [Lilian Weng](https://lilianweng.github.io/) - [Tim Dettmers](https://timdettmers.com/) - [Karpathy (home)](https://karpathy.github.io/) - [The Bitter Lesson](http://www.incompleteideas.net/IncIdeas/BitterLesson.html) --- ## Implementation / Hands-on - Coding models in PyTorch ([stable diffusion](https://www.youtube.com/watch?v=ZBKpAp_6TGI&ab_channel=UmarJamil), [LLM transformer](https://www.youtube.com/watch?v=kCc8FmEb1nY&t=41s&ab_channel=AndrejKarpathy)) - Code [DiT architecture](https://github.com/facebookresearch/DiT) from scratch based on the [paper](https://arxiv.org/abs/2212.09748) using only the trainer and diffusion module (no using LLMs for help) --- ## Communities, News & Social ### Twitter / X — people to follow (unfortunately, most of AI informal discourse happens on social media) - [Yannic Kilcher](https://x.com/ykilcher) - [Yann LeCun](https://x.com/ylecun) - [AK (@_akhaliq)](https://x.com/_akhaliq) — daily paper drops - [Allen AI](https://x.com/allen_ai) - [Chris Paxton](https://x.com/chris_j_paxton) - [Jim Fan](https://x.com/DrJimFan) - [Zeyuan Allen-Zhu](https://x.com/ZeyuanAllenZhu) - [Lucas Beyer (@giffmana)](https://x.com/giffmana) - [Clément Delangue (HuggingFace)](https://x.com/ClementDelangue) - [Andrej Karpathy](https://x.com/karpathy) - [Jeremy Howard](https://x.com/jeremyphoward) - [Lilian Weng](https://x.com/lilianweng) - [Soumith Chintala (PyTorch)](https://x.com/soumithchintala) - [Aran Komatsuzaki](https://x.com/arankomatsuzaki) — paper threads - [Sasha Rush](https://x.com/srush_nlp) - [Hardmaru (David Ha)](https://x.com/hardmaru) - [Sander Dieleman](https://x.com/sedielem) — great on diffusion - [Jürgen Schmidhuber](https://x.com/SchmidhuberAI) - [Tim Dettmers](https://x.com/Tim_Dettmers) ### Google Scholar — researchers I follow - [Yann LeCun](https://scholar.google.com/citations?user=WLN3QrAAAAAJ) - [Kaiming He](https://scholar.google.com/citations?user=DhtAFkwAAAAJ) - [Saining Xie](https://scholar.google.com/citations?user=Y2GtJkAAAAAJ) - [Yilun Du](https://scholar.google.com/citations?user=GRMMc_MAAAAJ) - [Mengye Ren](https://scholar.google.com/citations?user=XcQ9WqMAAAAJ) - [Percy Liang](https://scholar.google.com/citations?user=pouyVyUAAAAJ) - [Tatsunori Hashimoto](https://scholar.google.com/citations?user=5ygiTwsAAAAJ) ### Reddit - [r/MachineLearning](https://www.reddit.com/r/MachineLearning/) --- ## YouTube Channels - [Yannic Kilcher](https://www.youtube.com/@YannicKilcher) — paper walkthroughs - [bycloud](https://www.youtube.com/@bycloudAI) — research news roundups - [3Blue1Brown](https://www.youtube.com/@3blue1brown) — math intuition - [AI Explained](https://www.youtube.com/@aiexplained-official) - [Fireship](https://www.youtube.com/@Fireship) - [Jia-Bin Huang](https://www.youtube.com/@jbhuang0604) - [Steve Brunton (Eigensteve)](https://www.youtube.com/@Eigensteve) - [StatQuest](https://www.youtube.com/@statquest) - [Andrej Karpathy](https://www.youtube.com/@AndrejKarpathy) - [Deepia](https://www.youtube.com/@Deepia-ls2fo) - [Two Minute Papers](https://www.youtube.com/@TwoMinutePapers) - [Machine Learning Street Talk](https://www.youtube.com/@MachineLearningStreetTalk) — long-form interviews - [AI Coffee Break with Letitia](https://www.youtube.com/@AICoffeeBreak) - [Computerphile](https://www.youtube.com/@Computerphile) - [Sentdex](https://www.youtube.com/@sentdex) --- ## Other Resources - Sign up for [HuggingFace Papers](https://huggingface.co/papers) daily emails — curated top arxiv papers --- ## Notes & Advice - Rewatch/re-read things you didn't get initially after some time — progress is real but gradual. - Use LLMs to help explain papers and math (just be careful doing so) - My blog on how to do AI research (coming soon — feel free to ask me for the draft)
