All episodes
VIDEO SHORT | MIT 6.036: Why AI Uses Random Noise to Learn Faster
VIDEO | MIT 6.036: Visualizing Gradient Descent & SGD Loss Landscapes
Title: EP 61 | MIT 6.036: Gradient Descent & Optimization in Practice
VIDEO SHORT | MIT 6.036: Why Step Functions Break AI Optimization
VIDEO | MIT 6.036: Visualizing Logistic Regression & Loss Landscapes
EP 60 | MIT 6.036: Logistic Regression & ML as Optimization
VIDEO SHORT | MIT 6.036: Feature Representation
VIDEO | MIT 6.036: Feature Representation
EP 59 | MIT 6.036: Feature Representation
VIDEO SHORT | MIT 6.036: The Perceptron Algorithm
VIDEO | MIT 6.036: The Perceptron Algorithm
EP 58 | MIT 6.036: The Perceptron Algorithm
VIDEO SHORT | MIT 6.036: Is Your AI Learning or Memorizing?
VIDEO | MIT 6.036: Visualizing Linear Classifiers & ML Foundations
EP 57 | MIT 6.036: Foundations of ML & Linear Classifiers
VIDEO | CS224N: The Complete Course in 20 Minutes
SHORT | CS224N: Open areas in NLP
EP 56 | CS224N: Open Frontiers in NLP & The Smart Scaling Era
SHORT VIDEO | CS224N: Multimodality
EP 55 | CS224N: Multimodality
SHORT VIDEO | CS224N: Social Impacts of NLP
SHORT VIDEO | CS224N: Interpretability
EP 54 | CS224N: Social and broader impacts of NLP
EP 53 | CS224N: Model Interpretability & Editing
Video Short: Tokenization & Multilinguality
EP 52 | CS224N: Tokenization & Multilinguality
EP 51 | CS224N: AI Reasoning (Part 2)
EP 50 | CS224N: Reasoning Part 1
EP 49 | CS224N: Benchmarking and Evaluation
EP 48 | CS224N: RAG and Language Agents
EP 47 | CS224N: Efficient Adaptation
EP 46 | CS224N: Post-training
EP 45 | CS224N: Pre-training
EP 44 | CS224N: Transformers
EP 43 | CS224N: Language Models and RNNs
EP 42 | CS224N: Backpropagation and Neural Networks
EP 41 | CS224N: Word Vectors
EP 40 | CS224N: History of NLP
EP 39 | CME295 in 15 Minutes (The Full Recap)
EP 38 | CME295: Recap & Future Trends
EP 37 | CME295: LLM Evaluations
EP 36 | CME295: Agentic LLMs
EP 35 | CME295: LLM Reasoning
EP 34 | CME295: LLM Tuning
EP 33 | CME295: LLM Training
EP 32 | CME295: Large Language Models (LLMs)
EP 31 | CME295: Transformer Architecture
EP 30 | CME295: Introduction to Transformers
EP 29 | CS25 in 10 Minutes (The Full Recap)
EP 28 | CS25: Transformers for Video Generation
EP 27 | CS25: World Models for Medicine
EP 26 | CS25: Transformers in Diffusion Models
EP 25 | CS25: The Biology of LLMs
EP 24 | CS25: Artificial General Intelligence (AGI)
EP 23 | CS25: Reinforcement Learning (RL)
EP 22 | CS25: Overview of Transformers
EP 21 | Duke ML for PMs in 10 Minutes (The Full Recap)
EP 20 | Duke ML for PMs: Tree Models & Ensembles
EP 19 | Duke ML for PMs: Linear Models
EP 18 | Duke ML for PMs: Model Evaluation & Business Metrics
EP 17 | Duke ML for PMs: The Modeling Process
EP 16 | Duke ML for PMs: Machine Learning Fundamentals
EP 15 | CS21SI: AI for Social Good in 10 Minutes (The Full Recap)
EP 14 | CS21SI Finale: Reinforcement Learning & The Future of Conservation
EP 13 | CS21SI: Computer Vision & Human Agency
Title: EP 12 | CS21SI: NLP, Transformers & Automated Disinformation
Title: EP 11 | CS21SI: Deep Learning for social good
EP 10 | CS21SI Week 1: ML Basics & The Ethics of AI
CS124 in 7 minutes
CS221 in 7 minutes
EP 09 | CS221 Week 6: Bayesian Learning, Logic & Final Wrap-up
EP 08 | CS221 Week 5: Markov Networks, Bayesian Networks & Inference
EP 07 | CS221 Week 4: Games & Constraint Satisfaction Problems
EP 06 | CS221 Week 3: Structured Perceptrons, A* Search & Markov Models
EP 05 | CS221 Week 2: Optimization & The Search for Solutions
EP 04 | CS221 Week 1: AI History, Ethics & ML Foundations
EP 03 | CS124 Week 3: Speech, Recommendations & Networks
EP 02 | CS124 Week 2: Embeddings, LLMs & Transformers
EP 01 | CS124 Week 1: Words, N-grams & Information Retrieval