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2026-09-01
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2025-07-2270
- Street Fighting Transformers
- How might LLMs store facts | Deep Learning Chapter 7
- What Matters for Model Merging at Scale?
- Re-thinking Transformers: Searching for Efficient Linear Layers over a Continuous Space of...
- AI for science with Sir Paul Nurse, Demis Hassabis, Jennifer Doudna, and John Jumper
- Normalization models of attention
- How difficult is AI alignment? | Anthropic Research Salon
- Building Anthropic | A conversation with our co-founders
- (Ep.73) DeepSeek CEO interview in English.
- Can Latent Program Networks Solve Abstract Reasoning?
- LSTM: The Comeback Story?
- DeepMind x UCL RL Lecture Series - Policy-Gradient and Actor-Critic methods [9/13]
- How DeepSeek Rewrote the Transformer [MLA]
- ARC-AGI-2 Overview With Francois Chollet
- Causal Representation Learning: A Natural Fit for Mechanistic Interpretability
- Advancing AI Reasoning - From Games to Complex Problem Solving | NVIDIA GTC 2025 Session
- How to write a fast Softmax kernel
- SemiAnalysis Founder Dylan Patel on New AI Regulations, Chinese AI & xAI's Surge to Hyperscale
- How To Think About Thinking Models
- TPU V4 and Trends in Accelerator Hardware - Mike Hutton
- On the Biology of a Large Language Model (Part 2)
- V.O. Complete. A masterclass from the pioneer of artificial intelligence. Jürgen Schmidhuber
- ORIGINAL FATHER OF AI ON DANGERS! (Prof. Jürgen Schmidhuber)
- Neel Does Research (Vibe Coding Edition)
- What is the Transformers’ Context Window in Deep Learning? (and how to make it LONG)
- Autoencoders | Deep Learning Animated
- AI Olympics (multi-agent reinforcement learning)
- DeepMind’s AlphaEvolve AI: History In The Making!
- DeepSeek's GRPO (Group Relative Policy Optimization) | Reinforcement Learning for LLMs
- Diffusion Models: DDPM | Generative AI Animated
- What is a Transformer? (Transformer Walkthrough Part 1/2)
- Diffusion Models From Scratch | Score-Based Generative Models Explained | Math Explained
- The Unreasonable Effectiveness of JPEG: A Signal Processing Approach
- More Than Image Generators: A Science of Problem-Solving using Probability | Diffusion Models
- ICML 2024 Tutorial"Machine Learning on Function spaces #NeuralOperators"
- The Breakthrough Behind Modern AI Image Generators | Diffusion Models Part 1
- 17.12.2024: Flow-based Models (Part 2)
- When AI Is Designed Like A Biological Brain
- Fireside Chat With Ilya Sutskever and Jensen Huang AI Today and Vision of the Future March 2023
- Information Theory for Language Models: Jack Morris
- All the neurons of a neural network learning the sine function : network with 1 neuron per layer
- Zed Inferred: Diffusion Language Models
- Matt Squire - Diving into Transformer Model Internals | PyData London 25
- I Visualised Attention in Transformers
- #22 Dylan Patel: China’s Robotics Dominance; AI Infrastructure Breakdown
- Energy-Based Transformers are Scalable Learners and Thinkers (Paper Review)
- The Attention Mechanism in Large Language Models
- Andrew Ng: Opportunities in AI - 2023
- Large Language Models in Five Formulas
- Stanford CS25: V2 I Introduction to Transformers w/ Andrej Karpathy
- Dense Associative Memory in Machine Learning
- Navigating Progress in AI and Neuroscience
- An overview of Generative AI: music, video and image creation
- Memristors for Analog AI Chips
- V-JEPA: Revisiting Feature Prediction for Learning Visual Representations from Video (Explained)
- LoRA explained (and a bit about precision and quantization)
- How to Build an LLM from Scratch | An Overview
- Transformer Neural Network: Visually Explained
- Floating Points are no more, Changes everything for LLMs!!!
- Sitan Chen - Provably learning a multi-head attention layer - IPAM at UCLA
- AI Art Explained: How AI Generates Images (Stable Diffusion, Midjourney, and DALLE)
- Stable Diffusion in Code (AI Image Generation) - Computerphile
- Tutorial | LLMs in 5 Formulas (360°)
- Let's build GPT: from scratch, in code, spelled out.
- Sholto Douglas & Trenton Bricken - How LLMs Actually Think
- What's next for AI agentic workflows ft. Andrew Ng of AI Fund
- How fly neurons compute the direction of visual motion
- The Most Important Algorithm in Machine Learning
- If we don’t get AGI by GPT-7 (~$1T), will we just never get it? – Sholto Douglas & Trenton Bricken
- How Diffusion Works for Text
2025-07-214
2025-07-15
2025-07-09
2025-07-02
2025-06-28
2025-06-272
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2025-05-167
- Optimizing Transformer-Based Diffusion Models for Video Generation with NVIDIA TensorRT
- You could have designed state of the art positional encoding
- attention is logarithmic, actually
- AI Arrives In The Middle East: US Strikes A Deal with UAE and KSA – SemiAnalysis
- Transformers Represent Belief State Geometry in their Residual Stream
- Llama from scratch (or how to implement a paper without crying)
- The MAP-Elites Algorithm: Finding Optimality Through Diversity
2025-05-13
2025-05-06
2025-05-04
2025-05-032
2025-05-01
2025-04-24
2025-04-225
- Training Large Language Models to Reason in a Continuous Latent Space
- On the Biology of a Large Language Model
- Do Llamas Work in English? On the Latent Language of Multilingual Transformers
- The Era of Experience Paper
- "Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?"
2025-04-192
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- Unnamed Document
- New Scaling Laws for Large Language Models
- Binary Magic: Building BitNet 1.58bit Using PyTorch from Scratch
- king - man + woman is queen; but why?
- How Good Are Low-bit Quantized LLaMA3 Models? An Empirical Study
- 1-bit Model
- Human Knowledge Compression Contest
- Heatmaps and CNNs Using Fast.ai
2024-05-194
2024-05-10
2024-05-013
2024-04-25
2024-04-10
2024-04-09
2024-04-06
2024-03-28
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2024-03-08
2024-03-065
- The Art of Embeddings: Transforming Text for Vector Databases (Part 2)
- Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction Tuning on General Tasks
- þÿThe Little Book of Deep Learning
- Sequence to Sequence Learning with Neural Networks
- The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
2024-02-28
2024-02-26
2024-02-18
2024-02-16
2024-02-10
2024-02-09
2024-02-082
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2024-01-20
2024-01-152
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2024-01-12
2024-01-106
- This project is about how to systematically persuade LLMs to jailbreak them.
- Pruning vs Quantization: Which is Better?
- mlx-examples/lora at main · ml-explore/mlx-examples · GitHub
- Mixtral of Experts
- Paper page - Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models
- From LLM to Conversational Agent: A Memory Enhanced Architecture with Fine-Tuning of Large Language Models
2024-01-09
2024-01-08
2024-01-077
- Understanding The Exploding and Vanishing Gradients Problem
- Practical Deep Learning for Coders 2022
- fastai/fastbook: The fastai book, published as Jupyter Notebooks
- Elasticsearch 8.x Cookbook: Over 180 recipes to perform fast, scalable, and reliable searches for your enterprise, 5th Edition
- Attention? Attention!
- An Intuition for Attention
- Transformers From Scratch
2024-01-053
2024-01-0410
- Tensor2Tensor Intro
- The Annotated Transformer
- The Illustrated Transformer
- GitHub - tensorflow/nmt: TensorFlow Neural Machine Translation Tutorial
- What Are Word Embeddings for Text?
- Deep Learning for Natural Language Processing
- Visualizing A Neural Machine Translation Model (Mechanics of Seq2seq Models With Attention)
- The Random Transformer
- GitHub - SkalskiP/courses: This repository is a curated collection of links to various courses and resources about Artificial Intelligence (AI)
- CS25: Transformers United V3
2024-01-034
2024-01-02
2024-01-01
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