Jeremy Howard — Person Summaries
AI-powered summaries of 9 videos about Jeremy Howard.
9 summaries
The Epic History of Large Language Models (LLMs) | From LSTMs to ChatGPT | CampusX
Large language models didn’t appear out of nowhere—they’re the result of a decade-long chain of fixes to how neural networks handle language...
Claude 3 Opus is the best AI LLM - Open AI is Sweating?
Anthropic’s Claude 3—especially the Opus model—lands with benchmark results that put it ahead of GPT-4 across key areas like graduate-level...
Lecture 1: Introduction to Deep Learning - Full Stack Deep Learning - March 2019
Deep learning’s breakthrough in 2012 wasn’t just a better model—it replaced hand-crafted image features with learned representations, turning “what...
Lecture 02: Development Infrastructure & Tooling (FSDL 2022)
Machine learning development runs on a “data flywheel,” but getting from an idea to a reliable system at scale depends on disciplined software...
Jeremy Howard on Platform.ai and Fast.ai (Full Stack Deep Learning - March 2019)
Jeremy Howard argues that “augmented machine learning”—tight human–computer collaboration—beats fully automated ML pipelines for most practical...
Livecoding: Getting Started with LLMs, by Jeremy Howard
The core takeaway is that strong performance on an LLM multiple-choice science benchmark comes less from clever prompting and more from disciplined...
Software Engineering (2) - Infrastructure and Tooling - Full Stack Deep Learning
Python has become the default language for full-stack deep learning less because it’s inherently perfect for scientific computing and more because...
Labeling (3) - Data Management - Full Stack Deep Learning
Data labeling hinges less on the annotation software’s feature list and more on the human decisions inside the labeling workflow—especially when...
Sources (2) - Data Management - Full Stack Deep Learning
Deep learning in production often hinges less on flashy model design and more on how teams source, label, and multiply data. Label-hungry approaches...