YouTube: Stanford Online
AI news source. Articles are auto-selected and adapted by Hamidun News editors.
Latest publications
Stanford Posts LLM Training Lecture: 16 Chapters From Pretraining to GRPO
Stanford's third CME295 lecture walks through the full LLM training stack in 16 chapters: pretraining, SFT, LoRA, RLHF, DPO, GRPO and on-policy distillation.
Stanford Posts Marco Pavone's Lecture on Graph-Based Robot Motion Planning
Stanford Online has published Lecture 6 of AA274A, on graph search for path planning, taught by NVIDIA autonomous vehicle researcher Marco Pavone. The recording is from 2019; the textbook is free.
Stanford Online Posts Lecture 12 of AA274A: Classical Computer Vision for Robots
Marco Pavone's lecture from the 2019 Principles of Robotic Autonomy course covers information extraction and classic visual recognition. The textbook is open, and the syllabus is up to date.
Stanford Breaks Down the Retina as an Interpretable Neural Network in Course EE292R
In Stanford's EE292R, Stephen Baccus shows how mechanistic interpretability explains retinal computation: from 100 million photoreceptors to 1 million optic nerve fibers.
Stanford Posts Pavone's Stereo Vision Lecture: AA274A Course and a Free Textbook
Stanford Online has published Lecture 10 of the 2019 AA274A course on stereo vision. Marco Pavone of Stanford and NVIDIA teaches it, and a free PoRA textbook accompanies the course.
Stanford Online: AI Can Estimate Impact and Cost, but a Human Sets the Priority
Stanford's Anand Subramani and Laurel CPO Jiaona Zhang reduce roadmap prioritization to three steps: AI helps with two, and the third stays with a human.
Stanford Online Posts Lecture 8 of AA274A on Robot Sensors and Computer Vision
Stanford Online has published a lecture by Marco Pavone from the 2019 course: robot sensors and an introduction to computer vision. The Principles of Robot Autonomy textbook is free.
Stanford posts Pavone's motion planning lecture: RRT, PRM and FMT*
Lecture 7 of Stanford's 2019 AA274A course is now on YouTube. Marco Pavone covers sampling-based motion planning, from PRM and RRT to the asymptotically optimal RRT*.