ML Fundamentals

Why neural networks are so deep? (AlexNet - Explained)

Visualizing convolution networks

#313 Better Predictive Models with Graph Transformers | Jure Leskovec, Professor at Stanford

Why convolution networks work so well (on images)

Reinforcement learning with Unitree G1 humanoid - Dev w/ G1 P.5

How Natera scales genomics with Amazon SageMaker Catalog | Amazon Web Services

Structuring a modern AI team — Denys Linkov, Wisedocs

Akiko Funaki | Advanced Management Leadership Programme

Configure & use Amazon FSx for Lustre with Amazon EKS Pods | Amazon Web Services

Lecture 5: Sums

Convolution Network back propagation by hand | the math you should know!

Arthur Douillard - Distributed Training in Machine Learning

Revolutionizing Neural Network Training: Tony S.F. Explains Optimization with LMO

Sponsored by: SAP | SAP and Databricks Open a Bold New Era of Data and AI​

Databricks as the Backbone of MLOps: From Orchestration to Inference

TAO and Reinforcement Learning: Building AI With the Data You Have

Future-Ready Data at Scale: How Shutterfly Modernized for GenAI-Driven Personalization

Webinar: Alumni in Action: Oxford Insights and Sustainability Leadership

Tony Silveti Falls - Training neural networks at any scale

Stanford AA222 I Engineering Design Optimization | Spring 2025 | Disciplined Convex Programming

no distributed training

Back propagation by hand | the math you should know

The State of AI Powered Search and Retrieval — Frank Liu, MongoDB (prev Voyage AI)

Andrej Jovanovic -Communication efficient training for foundation models through federated learning