CS 285: Lecture 23, Part 2: Challenges & Open Problems

CS 285: Guest Lecture: Aviral Kumar

CS 285: Guest Lecture: Aviral Kumar

CS 285: Lecture 23, Part 1: Challenges & Open Problems

CS 285: Lecture 23, Part 1: Challenges & Open Problems

How to Speak

How to Speak

CS 285: Lecture 4, Part 1

CS 285: Lecture 4, Part 1

CS 285: Lecture 21, RL with Sequence Models & Language Models, Part 1

CS 285: Lecture 21, RL with Sequence Models & Language Models, Part 1

CS 285: Eric Mitchell: Reinforcement Learning from Human Feedback: Algorithms & Applications

CS 285: Eric Mitchell: Reinforcement Learning from Human Feedback: Algorithms & Applications

Yann LeCun: Why RL is overrated | Lex Fridman Podcast Clips

Yann LeCun: Why RL is overrated | Lex Fridman Podcast Clips

The paradox of the derivative | Chapter 2, Essence of calculus

The paradox of the derivative | Chapter 2, Essence of calculus

CS 285: Lecture 6, Part 1

CS 285: Lecture 6, Part 1

Offline Reinforcement Learning: BayLearn 2021 Keynote Talk

Offline Reinforcement Learning: BayLearn 2021 Keynote Talk

Lecture 2 CS329A Jan 10

Lecture 2 CS329A Jan 10

Reinventing Entropy | Compression is Intelligence Part 1

Reinventing Entropy | Compression is Intelligence Part 1

Transformers, the tech behind LLMs | Deep Learning Chapter 5

Transformers, the tech behind LLMs | Deep Learning Chapter 5

Natasha Jaques PhD Thesis Defense

Natasha Jaques PhD Thesis Defense

CS 285: Lecture 21, RL with Sequence Models & Language Models, Part 2

CS 285: Lecture 21, RL with Sequence Models & Language Models, Part 2

This Is How OpenAI Goes Broke

This Is How OpenAI Goes Broke

Course Overview

Course Overview

6. Monte Carlo Simulation

6. Monte Carlo Simulation

Gradient descent, how neural networks learn | Deep Learning Chapter 2

Gradient descent, how neural networks learn | Deep Learning Chapter 2