Published inData Science CollectiveLearning Triton One Kernel at a Time: SoftmaxAll you need to know to write a fast, readable and PyTorch-ready softmax kernel!Dec 27, 2025A response icon1Dec 27, 2025A response icon1
Published inData Science CollectiveLearning Triton One Kernel at a Time: Matrix MultiplicationLearn about efficient matrix multiplication, memory hierarchy in modern GPUs, coalescing and much more!Nov 14, 2025A response icon4Nov 14, 2025A response icon4
Published inData Science CollectiveLearning Triton One Kernel At a Time: Vector AdditionThe basics of GPU programming, optimisation, and your first Triton kernel!Oct 29, 2025A response icon4Oct 29, 2025A response icon4
Published inTDS ArchiveRainbow: The Colorful Evolution of Deep Q-Networks 🌈Everything you need to assemble the DQN Megazord in JAX.Jul 12, 2024Jul 12, 2024
Published inTDS ArchiveA Practical Guide to Proximal Policy Optimization in JAXAll the tricks and details you wish you knew about PPOMay 1, 2024A response icon1May 1, 2024A response icon1
Published inTDS ArchiveA Gentle Introduction to Deep Reinforcement Learning in JAXSolving the CartPole environment with DQN in under a secondNov 21, 2023A response icon2Nov 21, 2023A response icon2
Published inTDS ArchiveImplementing a Transformer Encoder from Scratch with JAX and Haiku 🤖Understanding the fundamental building blocks of Transformers.Nov 7, 2023A response icon3Nov 7, 2023A response icon3
Published inTDS ArchiveVectorize and Parallelize RL Environments with JAX: Q-learning at the Speed of Light⚡Learn to vectorize a GridWorld environment and train 30 Q-learning agents in parallel on a CPU, at 1.8 million step per seconds!Oct 15, 2023A response icon2Oct 15, 2023A response icon2
Published inTDS ArchiveTemporal-Difference Learning and the importance of exploration: An illustrated guideA comparison of model-free (Q-learning) and model-based (Dyna-Q and Dyna-Q+) TD methods on a dynamic grid world.Sep 23, 2023A response icon3Sep 23, 2023A response icon3
