[Practical Deep Learning for Coders - Practical Deep Learning](https://course.fast.ai/) Machine Learning with PyTorch and Scikit-Learn [Amazon.in](https://www.amazon.in/Machine-Learning-PyTorch-Scikit-Learn-learning/dp/1801819319/?_encoding=UTF8&pd_rd_w=pY8lK&content-id=amzn1.sym.1dbfbf40-4af3-4617-869f-d5f26f948dda%3Aamzn1.symc.fddc876e-12b4-4705-b1ac-5d8fc5d10d1f&pf_rd_p=1dbfbf40-4af3-4617-869f-d5f26f948dda&pf_rd_r=6PW1BH914AEBPVCKWQH1&pd_rd_wg=AwFXF&pd_rd_r=ad6fea0c-1a38-4813-8faa-4060670a294f&ref_=pd_hp_d_atf_ci_mcx_mr_ca_hp_atf_d) [https://probml.github.io/pml-book/book1.html](https://probml.github.io/pml-book/book1.html) [Understanding Deep Learning](https://udlbook.github.io/udlbook/) Artificial Intelligence Math Olympiad (AIMO) with LLMs https://x.com/Thom_Wolf/status/1809895886899585164?t=57Zl4N1dg0MYFZbR2JDjyg&s=19 Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow Concepts, Tools, and Techniques to Build Intelligent Systems [http://14.139.161.31/OddSem-0822-1122/Hands-On_Machine_Learning_with_Scikit-Learn-Keras-and-TensorFlow-2nd-Edition-Aurelien-Geron.pdf](http://14.139.161.31/OddSem-0822-1122/Hands-On_Machine_Learning_with_Scikit-Learn-Keras-and-TensorFlow-2nd-Edition-Aurelien-Geron.pdf) [The Complete Data Science Pathway Courses by ML+](https://edu.machinelearningplus.com/s/pages/ds-career-path) Deriving 3D Rigid Body Physics and implementing it in C/C++ (with intuitions) [https://www.youtube.com/watch?v=4r_EvmPKOvY](https://www.youtube.com/watch?v=4r_EvmPKOvY) Transformer [https://www.youtube.com/watch?v=rPFkX5fJdRY](https://www.youtube.com/watch?v=rPFkX5fJdRY) Engineering Math: Differential Equations and Dynamical Systems [Než budete pokračovat na YouTube](https://www.youtube.com/playlist?list=PLMrJAkhIeNNTYaOnVI3QpH7jgULnAmvPA) emergence [[2402.09090] Software in the natural world: A computational approach to hierarchical emergence](https://arxiv.org/abs/2402.09090) https://x.com/_fernando_rosas/status/1758096442810405162 https://journals.aps.org/pre/abstract/10.1103/PhysRevE.108.014304 generalization [[2402.03507] Neural networks for abstraction and reasoning: Towards broad generalization in machines](https://arxiv.org/abs/2402.03507) Platonic representation hypothesis [Než budete pokračovat na YouTube](https://www.youtube.com/live/1_xH2mUFpZw?si=CpwyJKCcmGhE4dIK) Fine-tuning Program synthesis [a minimalist guide to program synthesis | a bare minimum introduction to modern program synthesis](https://evanthebouncy.github.io/program-synthesis-minimal/) https://towardsdatascience.com/foundation-models-in-graph-geometric-deep-learning-f363e2576f58 Semantic search for mathematical models of intelligence on arxiv [arXiv Xplorer](https://arxivxplorer.com/?query=https%3A%2F%2Farxiv.org%2Fabs%2F1911.01547) Claude Interpreter: Taking Safe AI to Market with Alex Albert of Anthropic [https://www.youtube.com/watch?v=5cQouQZm9fI](https://www.youtube.com/watch?v=5cQouQZm9fI) Noether's Learning Dynamics: Role of Symmetry Breaking in Neural Networks [[2105.02716] Noether's Learning Dynamics: Role of Symmetry Breaking in Neural Networks](https://arxiv.org/abs/2105.02716) Evolutionary Implications of Self-Assembling Cybernetic Materials with Collective Problem-Solving Intelligence at Multiple Scales https://www.mdpi.com/1099-4300/26/7/532 Hutter formalism of AGI [https://www.youtube.com/watch?v=7TgOwMW_rnk&feature=youtu.be](https://www.youtube.com/watch?v=7TgOwMW_rnk&feature=youtu.be) https://x.com/IAmTimNguyen/status/1788964432632066457 Artificial life [Leniabreeder](https://leniabreeder.github.io/) https://x.com/maxencefaldor/status/1803803486179434642?t=6eJjBHLt95EH7n7GBmpcIA&s=19 https://x.com/BertChakovsky/status/1804040248332284107?t=WUOj_TMy0BhTAgNbseI0OQ&s=19 My philosophical glue is better than your philosophical glue! (Gluing together predictive or/and nonpredictive models) PCA [Reddit - The heart of the internet](https://www.reddit.com/r/learnmachinelearning/comments/1dlnx4o/best_mlai_online_program/) [Reddit - The heart of the internet](https://www.reddit.com/r/learnmachinelearning/comments/1d0bksx/i_scraped_and_ranked_ai_courses_here_are_the_best/) Reverse engineering world models [[2406.03689] Evaluating the World Model Implicit in a Generative Model](https://arxiv.org/abs/2406.03689) https://x.com/keyonV/status/1803838591371555252?t=OvX5fqwR7HeCgMZ1g3RHQw&s=19 Solutions to ARC [https://youtu.be/jSAT_RuJ_Cg?si=a2WPVq-d73IUtmfg](https://youtu.be/jSAT_RuJ_Cg?si=a2WPVq-d73IUtmfg) Accelerating grokking [[2405.20233] Grokfast: Accelerated Grokking by Amplifying Slow Gradients](https://arxiv.org/abs/2405.20233) https://x.com/_ironjr_/status/1798733867303772607?t=ap-se3Q1rJ5IRm5veEMHKw&s=19 https://x.com/davidad/status/1804550585124864123?t=3p6pDrDs3TSBCuazqoHQWg&s=19 Sutton Reinforcement Learning for Continuing Problems Using Average Reward https://x.com/RichardSSutton/status/1804597434070507825?t=ypAZwro-ji-TBA9nR-paLw&s=19 AI books https://x.com/FrnkNlsn/status/1804396517748543593?t=1aDV-r4rYrb8S4qXoFubrg&s=19 https://x.com/FrnkNlsn/status/1805873981293318424?t=AzVXy42N6wvsgWjKx5hm_A&s=19 Flow matching Mechanistic interpretability generalization [[2405.15071] Grokked Transformers are Implicit Reasoners: A Mechanistic Journey to the Edge of Generalization](https://arxiv.org/abs/2405.15071) Witten [https://www.youtube.com/watch?v=2UQ8teAebcg](https://www.youtube.com/watch?v=2UQ8teAebcg) https://www.anthropic.com/research/mapping-mind-language-model https://openai.com/index/extracting-concepts-from-gpt-4/ [https://www.youtube.com/watch?v=8Nyn3_ZWa_U](https://www.youtube.com/watch?v=8Nyn3_ZWa_U) https://towardsdatascience.com/foundation-models-in-graph-geometric-deep-learning-f363e2576f58 Anthropic interpretability [https://www.youtube.com/watch?v=8Nyn3_ZWa_U](https://www.youtube.com/watch?v=8Nyn3_ZWa_U) Algebraic topology [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PLOROtRhtegr7DmeMyFxfKxsljAVsAn_X4&si=r8MhU6WHfXnCGK2n) Neural network mechanistic interpretability Toy Models of Superposition [A Comprehensive Mechanistic Interpretability Explainer & Glossary - Dynalist](https://dynalist.io/d/n2ZWtnoYHrU1s4vnFSAQ519J) [Toy Models of Superposition](https://transformer-circuits.pub/2022/toy_model/index.html) [https://www.youtube.com/watch?v=R3nbXgMnVqQ](https://www.youtube.com/watch?v=R3nbXgMnVqQ) (Nanda) Supermasks in Superposition [[2006.14769] Supermasks in Superposition](https://arxiv.org/abs/2006.14769) INTERPRETABILITY IN THE WILD [https://arxiv.org/pdf/2211.00593.pdf](https://arxiv.org/pdf/2211.00593.pdf) Actually, Othello-GPT Has A Linear Emergent World Representation (Nanda) [Actually, Othello-GPT Has A Linear Emergent World Representation — LessWrong](https://www.lesswrong.com/s/nhGNHyJHbrofpPbRG/p/nmxzr2zsjNtjaHh7x) [Large Language Model: world models or surface statistics?](https://thegradient.pub/othello) A Mathematical Framework for Transformer Circuits [A Mathematical Framework for Transformer Circuits](https://transformer-circuits.pub/2021/framework/index.html) [https://www.youtube.com/watch?v=KV5gbOmHbjU](https://www.youtube.com/watch?v=KV5gbOmHbjU) (Nanda) Attention is all you need [[1706.03762] Attention Is All You Need](https://arxiv.org/abs/1706.03762) A STRUCTURED SELF-ATTENTIVE SENTENCE EMBEDDING [A STRUCTURED SELF-ATTENTIVE SENTENCE EMBEDDING | OpenReview](https://openreview.net/forum?id=BJC_jUqxe) Distributed Representations of Words and Phrases and their Compositionality (Mikolov) [[1310.4546] Distributed Representations of Words and Phrases and their Compositionality](https://arxiv.org/abs/1310.4546) Deep Residual Learning for Image Recognition [[1512.03385] Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) Attribution Patching: Activation Patching At Industrial Scale (Nanda) [Attribution Patching: Activation Patching At Industrial Scale — Neel Nanda](https://www.neelnanda.io/mechanistic-interpretability/attribution-patching) In-context Learning and Induction Heads [In-context Learning and Induction Heads](https://transformer-circuits.pub/2022/in-context-learning-and-induction-heads/index.html) [https://www.youtube.com/watch?v=dCkQQYwPxdM](https://www.youtube.com/watch?v=dCkQQYwPxdM) (Nanda) The Quantization Model of Neural Scaling [https://arxiv.org/pdf/2303.13506.pdf](https://arxiv.org/pdf/2303.13506.pdf) Interpreting Neural Networks to Improve Politeness Comprehension [Interpreting Neural Networks to Improve Politeness Comprehension - ACL Anthology](https://aclanthology.org/D16-1216/) Progress measures for grokking via mechanistic interpretability [[2301.05217] Progress measures for grokking via mechanistic interpretability](https://arxiv.org/abs/2301.05217) (Nanda) [https://www.youtube.com/watch?v=IHikLL8ULa4](https://www.youtube.com/watch?v=IHikLL8ULa4) https://twitter.com/NeelNanda5/status/1616590887873839104 Grokking paper [[2201.02177] Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets](https://arxiv.org/abs/2201.02177) A Toy Model of Universality [[2302.03025] A Toy Model of Universality: Reverse Engineering How Networks Learn Group Operations](https://arxiv.org/abs/2302.03025) https://twitter.com/bilalchughtai_/status/1625948104121024516 A circuit for Python docstrings in a 4-layer attention-only transformer [A circuit for Python docstrings in a 4-layer attention-only transformer — LessWrong](https://www.lesswrong.com/posts/u6KXXmKFbXfWzoAXn/a-circuit-for-python-docstrings-in-a-4-layer-attention-only) Fodor, J. A., & Pylyshyn, Z. W. (1988). Connectionism and cognitive architecture: A critical analysis. [APA PsycNet](https://psycnet.apa.org/record/1989-03804-001) Maximal Update Parametrization (μP) and Hyperparameter Transfer (μTransfer) [GitHub - microsoft/mup: maximal update parametrization (µP)](https://github.com/microsoft/mup) Spline theory of NNs [https://proceedings.mlr.press/v80/balestriero18b/balestriero18b.pdf](https://proceedings.mlr.press/v80/balestriero18b/balestriero18b.pdf) Counterarguments to the basic AI x-risk case (Katja Grace) [Counterarguments to the basic AI x-risk case — LessWrong](https://www.lesswrong.com/posts/LDRQ5Zfqwi8GjzPYG/counterarguments-to-the-basic-ai-x-risk-case) The alignment problem from a deep learning perspective (Ngo) [[2209.00626] The Alignment Problem from a Deep Learning Perspective](https://arxiv.org/abs/2209.00626) Superintelligence: Paths, Dangers, Strategies. [Amazon.co.uk](https://www.amazon.co.uk/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/0199678111) Quantum grokking [https://youtu.be/BtHMIQs_5Nw?si=P7wAtuEtEIO5eJH7](https://youtu.be/BtHMIQs_5Nw?si=P7wAtuEtEIO5eJH7) Formal mechanistic interpretability [Compact Proofs of Model Performance via Mechanistic Interpretability — AI Alignment Forum](https://www.alignmentforum.org/posts/bRsKimQcPTX3tNNJZ/compact-proofs-of-model-performance-via-mechanistic) https://x.com/diagram_chaser/status/1805337592143265801?t=fJdQa3bxJMXhQrqhVUIsMA&s=19 [Research Engineer @ Cartesia](https://jobs.ashbyhq.com/cartesia/c1f51855-9076-409f-a410-88df5d883b28) [[2310.02299] Discovering Symmetry Breaking in Physical Systems with Relaxed Group Convolution](https://arxiv.org/abs/2310.02299) Deep learning for physics https://x.com/yuqirose/status/1805327242790420610?t=u2NlvpRyN-D4ST1jtR5A5g&s=19 https://www.pnas.org/doi/10.1073/pnas.2311808121 Dan Hendricks Roman Salponsky AI Davidad mathematics https://x.com/davidad/status/1805494402497814979?t=ztLu33_c3mgcnd55zIBy9w&s=19 Amplituhedron in context learning mechanistic interpretability [In-context Learning and Induction Heads](https://transformer-circuits.pub/2022/in-context-learning-and-induction-heads/index.html) [[2402.13055v1] Identifying Semantic Induction Heads to Understand In-Context Learning](https://arxiv.org/abs/2402.13055v1) Mechanistic Interpretability for AI Safety -- A Review [[2404.14082] Mechanistic Interpretability for AI Safety -- A Review](https://arxiv.org/abs/2404.14082) Semiconductor fabrication [Než budete pokračovat na YouTube](https://www.youtube.com/playlist?list=PLM2eE_hI4gSDjK4SiDbhpmpjw31Xyqfo_) [Než budete pokračovat na YouTube](https://www.youtube.com/playlist?list=PLtkeUZItwHK4CUb6QuSOHJqMFjc8ITxmZ) [Než budete pokračovat na YouTube](https://www.youtube.com/playlist?list=PL4urpCsplnMF5kUm9FPk3iOnzNUnM-8tX) [Než budete pokračovat na YouTube](https://www.youtube.com/playlist?list=PLY7CknIBnYnBo9qEl1CfTfAeaVRg_3Ley) MIT deep learning in life sciences [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PLypiXJdtIca5sxV7aE3-PS9fYX3vUdIOX&si=A4Kpxy4w2CYuo_18) ETH Zurich deep learning in scientific computing [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PLJkYEExhe7rYY5HjpIJbgo-tDZ3bIAqAm&si=H7RGBCy42PHQrjIu) Scientific machine learning seminars [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PLw74xLHy0_j8DXxAKb15DbgtNvUOeTPbZ&si=vW3HJF_GdlVxKwHE) Statistics in machine learning [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PLZoTAELRMXVMhVyr3Ri9IQ-t5QPBtxzJO&si=Lj2ewGya_ZnGG43_) Brunton physics informed Machine learning [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PLMrJAkhIeNNQ0BaKuBKY43k4xMo6NSbBa&si=wR1J_xXBr6KaGYN8) [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PLWL3MaEZQ5I2LYm5BAdLYso5wGe9WMNby&si=Nxh86Tuzb2Iwa-5U) [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PLXmYoJbJ848pkMm9NGZZKXUQJ8XWIXZX8&si=Y3zaPO-Kbz4mHdno) Physics of machine learning [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PLHyI3Fbmv0SfQfS1rknFsr_UaaWpJ1EKA&si=Z-_QDI-43rk4t0L4) Statistical physics and machine learning [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PL04QVxpjcnjgzMr9ehyZUSkwu0Wr0cF_N&si=M27IgxuhkVghTlxP) [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PLo9ufcrEqwWE9JNBn1IWfOFHXiqEqur_r&si=7TUwRRpt5bpkM8Ia) [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PL04QVxpjcnjhtL3IIVyFRMOgdhWtPn7YJ&si=A0m4FNhUw64uLxs9) Statistical methods and machine learning in high energy physics [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PL04QVxpjcnjjKDki5FHlKQ8839TGHvj8y&si=Xj0WjBTR7ubN5bKe) Machine learning for condensed matter physics [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PLYc-eBoIpXTKSVBB2nUH6ezrMbT727Cka&si=2DAEPezuGQiJL-ha) Machine learning in genomics [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PLypiXJdtIca6U5uQOCHjP9Op3gpa177fK&si=Ja7ODGeY9CSvGbqO) [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PLypiXJdtIca6dEYlNoZJwBaz__CdsaoKJ&si=VBk9sxa1o5JmkS8g) Computational biology in python [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PLWVKUEZ25V94kdT2Lh97KqB9MoLV9ZzmU&si=kNcH2xm-yIggR8jP) Synthetic biology and machine learning [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PLf4eUKhxEIuruU51wt1b1waMyAOzkah5B&si=voezZdZVGBpjAGLJ) Bioinformatics python [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PLsSUJubNtkrCESp-eEiWayFa3vQ2VN3kK&si=bwjDqM6TpabbPn67) [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PLtqF5YXg7GLlQJUv9XJ3RWdd5VYGwBHrP&si=MAQz7MyrT05UfcRk) Machine learning in biology [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PLwU4WqkjjeXhHnNkoHuq-jQ3AgC3uhyhB&si=i0STqFddgK-NC4EM) [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PLj4Zs4bjsI9rigDItS7PMMvxrFv3WA_2_&si=WE10h_haohcFjmZp) [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PLJOFHNbp70IU8NrBtmTUFQ_0B8d0eRK2U&si=KX6EeIxAU2EOsgZO) Bioinformatics computational biology R [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PLRbUOpXOpZ2qGJuwhSA2XLkpb60Ly2auZ&si=AyqJ8RyjXKkMFVmK) Biotechnology Neurotechnology [https://www.youtube.com/watch?v=sNP4_3cbLxA](https://www.youtube.com/watch?v=sNP4_3cbLxA) Machine learning for neuroscience Synthetic biology Algebraic topology [https://youtu.be/XxFGokyYo6g?si=mSF6dBNVbXoFcW7q](https://youtu.be/XxFGokyYo6g?si=mSF6dBNVbXoFcW7q) Biologically plausible alternatives to backpropagation Error-driven Input Modulation: Solving the Credit Assignment Problem without a Backward Pass [[2201.11665] Error-driven Input Modulation: Solving the Credit Assignment Problem without a Backward Pass](https://arxiv.org/abs/2201.11665) [https://youtu.be/4kC-xVOUGDw?feature=shared](https://youtu.be/4kC-xVOUGDw?feature=shared) Symbolic Learning Enables Self-Evolving Agents [[2406.18532] Symbolic Learning Enables Self-Evolving Agents](https://arxiv.org/abs/2406.18532) Liquid neural networks [https://youtu.be/HGLjOxQxcr0?si=wf8EUNgLOa55BiTT](https://youtu.be/HGLjOxQxcr0?si=wf8EUNgLOa55BiTT) World models https://x.com/micheli_vincent/status/1806697975713866005?t=hfAZ9OAiQI-PmIhMj7RsmQ&s=19 [[2406.19320] Efficient World Models with Context-Aware Tokenization](https://arxiv.org/abs/2406.19320) The age of neurosymbolics is emerging [[2406.13892] Adaptable Logical Control for Large Language Models](https://arxiv.org/abs/2406.13892) https://x.com/HonghuaZhang2/status/1806727439823102325?t=B5ovBjcpq-AAGYO95ZrLow&s=19 Short Circuiting: the first alignment technique that is adversarially robust https://x.com/andyzou_jiaming/status/1799232319250743561 [https://www.promptingguide.ai/](https://www.promptingguide.ai/) biologically palusible neurons as controllers [New Computational Model of Real Neurons Could Lead to Better AI](https://www.simonsfoundation.org/2024/06/24/new-computational-model-of-real-neurons-could-lead-to-better-ai/) https://www.pnas.org/doi/10.1073/pnas.2311893121 Brain-Like Language Processing via a Shallow Untrained Multihead Attention Network [[2406.15109] Brain-Like Language Processing via a Shallow Untrained Multihead Attention Network](https://arxiv.org/abs/2406.15109) JEPA Constitutional AI RLHF from scratch [https://youtu.be/3uvnoVjM8nY?si=LiegzdgfTaHKLGc2](https://youtu.be/3uvnoVjM8nY?si=LiegzdgfTaHKLGc2) [https://youtu.be/CCTRyTAL72U?si=TKAEkGbUhwFxbiXq](https://youtu.be/CCTRyTAL72U?si=TKAEkGbUhwFxbiXq) Explainable AI [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PLV8yxwGOxvvovp-j6ztxhF3QcKXT6vORU&si=6Gyz5YA-sX_DEsdo) [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PLqDyyww9y-1SwNZ-6CmvfXDAOdLS7yUQ4&si=D2FIyST7FAynHN_K) [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PLj0wREcwABG40vlGIObmaj0B3TrJDl0iz&si=H2RKqwSs7MdigxOl) [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PL8_xPU5epJddcWuqIjPYBTHjKhAfnMdXN&si=prVWT8hILqkxs6oB) [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PLjvuMgo5HcNlsuARzur_t4Fw-6bJPE_no&si=_NvHs32nv1Ld4gsU) [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PL2SWJyNGIwtSIR5BxyJWstnYcorspJL1Q&si=E-dDhH6YHlW_WUvw) Interpretable AI [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PLWjm4hHpaNg6c-W7JjNYDEC_kJK9oSp0Y&si=bx6G8XQIbZproCvC) [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PLGViarxWrOJcIMxkMf0ALKICPzEKppkBE&si=kMEjAqkxwJLrOSnI) [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PLood5-0migxMD1jUeo10ezi0_j--fKTbm&si=Ib2-lZRHYdY5-u8U) [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PLuD_SqLtxSdXVSrXneEPkZtzTTQMT4hQ8&si=Zwbmv4qHcewfh8Iy) [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PLzERW_Obpmv-Jiqz1ODjdIokRJCc0eC-2&si=-RHNr8Seq2k8bnjF) [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PLdIuiWj7b66H_ajC97M390imZP87Ik7ZW&si=8lsQyFP7oBUKuwF0) [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PLFze15KrfxbE5jd5L4prQXwkUcSdwRboo&si=nynrfDknamuA6jF7) [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PLFVVASQZhO15JlwdBwSzrGC1Y_Qmbbj_K&si=yPp5L4IJsL94Ow0P) [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PLM5RAudXfaul-86LiRaeTCEr_w6AymEad&si=4HfoYvIH7yG4rF49) Explainable AI for sciences [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PLHyI3Fbmv0Sc0G5FXvUonb4j-fNNGDEPQ&si=HXZ5iPtAtQMEAedr) [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PLgKuh-lKre107ZnrJ3S0_DWoB3CHgJQpJ&si=UVzjXVN8_yUrSou-) Mixture of experts from scratch Neurosymbolic AI survey [[2305.08876] Neurosymbolic AI and its Taxonomy: a survey](https://arxiv.org/abs/2305.08876) [GitHub - nocotan/awesome-information-geometry: About A collection of AWESOME things about information geometry Topics](https://github.com/nocotan/awesome-information-geometry) Liquid neural networks Spiking neural networks Representations and generalization in artificial and brain neural networks https://www.pnas.org/doi/10.1073/pnas.2311805121 Reservoir computing Anil seth consciousness and AI models [OSF](https://osf.io/preprints/psyarxiv/tz6an) https://x.com/anilkseth/status/1807356419362189498?t=cJP5INhKcZWxTOTxJGHd7g&s=19 Consciousness theories landscape https://www.sciencedirect.com/science/article/pii/S0079610723001128 Differential geometry [Než budete pokračovat na YouTube](https://youtube.com/playlist?list=PL9_jI1bdZmz0hIrNCMQW1YmZysAiIYSSS&si=zMHgGnLU1gkaWgR-) Geometric deep learning [https://youtu.be/hROSXAY2JBc?si=xE2OEC9l_1qBtEcy](https://youtu.be/hROSXAY2JBc?si=xE2OEC9l_1qBtEcy) Machine learning meets program synthesis [https://youtu.be/vmBfyjF38ls?si=liVLtzksmJH9c1md](https://youtu.be/vmBfyjF38ls?si=liVLtzksmJH9c1md) Interpretable deep learning [https://youtu.be/Hvla3sgSXIc?si=INCkMDvl4CHTbElr](https://youtu.be/Hvla3sgSXIc?si=INCkMDvl4CHTbElr) Alternatives to Transformers [Reddit - The heart of the internet](https://www.reddit.com/r/MachineLearning/s/iN04FvgMDs) [Reddit - The heart of the internet](https://www.reddit.com/r/MachineLearning/s/rKiDH1K0oD) Optical neural networks https://techxplore.com/news/2024-06-convolutional-optical-neural-networks-herald.html Q* [[2406.14283] Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning](https://arxiv.org/abs/2406.14283) Information theory https://x.com/caglar_ee/status/1807641695074320764?t=5-zmh94tAwV7Syr_WCtvlQ&s=19 LoRA 3 hour animated math of electromagnetism [https://youtu.be/Sj_GSBaUE1o?si=eWByh_tluTQkWXJF](https://youtu.be/Sj_GSBaUE1o?si=eWByh_tluTQkWXJF) fun search alphafold [https://youtu.be/TqU-lCuSFUI?si=OjvV10AigfYxvsf9](https://youtu.be/TqU-lCuSFUI?si=OjvV10AigfYxvsf9) Higher topos theory in physics [https://youtu.be/Gr_BCr1rYVM?si=Yxju1g6buztcoqiE](https://youtu.be/Gr_BCr1rYVM?si=Yxju1g6buztcoqiE) [Toward A Mathematical Framework for Computation in Superposition — LessWrong](https://www.lesswrong.com/posts/2roZtSr5TGmLjXMnT/toward-a-mathematical-framework-for-computation-in) [https://www.youtube.com/results?search_query=thomas+metzinger](https://www.youtube.com/results?search_query=thomas+metzinger) philosopher all of experience is representations Conscious artificial intelligence and biological naturalism [OSF](https://osf.io/preprints/psyarxiv/tz6an) https://x.com/anilkseth/status/1807356419362189498 https://x.com/anilkseth/status/1807356444502847815 https://x.com/anilkseth/status/1807356461791744237 linear lagebra, numericl optimization, measure theory, convex optimization [[2405.14806] Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics](https://arxiv.org/abs/2405.14806) [GitHub - heidelberg-hepml/lorentz-gatr: Repository for <Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics> (J. Spinner et al 2024)](https://github.com/heidelberg-hepml/lorentz-gatr?tab=readme-ov-file) Free energy principle Hopfield networks Transformers in brain [Shared functional specialization in transformer-based language models and the human brain | Nature Communications](https://www.nature.com/articles/s41467-024-49173-5) https://x.com/sreejan_kumar/status/1807849677775106257?t=mbImCqEIIDejNRomyQmj7g&s=19 [Interpretable Machine Learning](https://christophm.github.io/interpretable-ml-book/) [Supervised Machine Learning for Science](https://ml-science-book.com/) supervised machine learning for science MLA+MOE next token prediction + full sequence diffusion [Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion](https://boyuan.space/diffusion-forcing/) machine learning books [https://franknielsen.github.io/Books/CuratedBookLists.html](https://franknielsen.github.io/Books/CuratedBookLists.html) [Reddit - The heart of the internet](https://www.reddit.com/r/learnmachinelearning/comments/1dtovf5/how_good_is_this_book_nowadays/) Bayesian deep learning [A Comprehensive Introduction to Bayesian Deep Learning - Joris Baan](https://jorisbaan.nl/2021/03/02/introduction-to-bayesian-deep-learning.html) Multimodal models https://x.com/reach_vb/status/1808528557431210236?t=qK6qkGVwx1O_RJ8kY1u0gQ&s=19 Langlands program [https://youtu.be/8RMqwNOQpOs?si=jNP16qFEwzqB0Jk7](https://youtu.be/8RMqwNOQpOs?si=jNP16qFEwzqB0Jk7) https://x.com/DiracGhost/status/1809033919737565585?t=j2OXL_rMezbJ3Ofdj3JSAA&s=19 Artificial Intelligence Math Olympiad (AIMO) with LLMs solutions [AIMO Prize](https://aimoprize.com/) https://x.com/Thom_Wolf/status/1809895886899585164?t=57Zl4N1dg0MYFZbR2JDjyg&s=19 [An Extremely Opinionated Annotated List of My Favourite Mechanistic Interpretability Papers v2 — AI Alignment Forum](https://www.alignmentforum.org/posts/NfFST5Mio7BCAQHPA/an-extremely-opinionated-annotated-list-of-my-favourite-1) Scalable oversight [[2407.04622] On scalable oversight with weak LLMs judging strong LLMs](https://arxiv.org/abs/2407.04622)