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Stanford CS231n: Convolutional Neural Networks for Visual Recognition →0/16
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Lecture 1 | Introduction to Convolutional Neural Networks for Visual Recognition

57:56
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Lecture 2 | Image Classification

59:31
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Lecture 3 | Loss Functions and Optimization

1:14:40
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Lecture 4 | Introduction to Neural Networks

1:13:59
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Lecture 5 | Convolutional Neural Networks

1:08:56
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Lecture 6 | Training Neural Networks I

1:20:19
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Lecture 7 | Training Neural Networks II

1:15:30
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Lecture 8 | Deep Learning Software

1:18:07
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Lecture 9: CNN Architectures (CS231n)

1:17:40
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Lecture 10: Recurrent Neural Networks (CS231n)

1:13:09
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Lecture 11: Detection and Segmentation (CS231n)

1:14:26
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Lecture 12: Visualizing and Understanding (CS231n)

1:15:48
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Lecture 13: Generative Models (CS231n)

1:17:41
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Lecture 14: Deep Reinforcement Learning (CS231n)

1:04:01
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Lecture 15: Efficient Methods and Hardware for Deep Learning (CS231n)

1:16:52
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Lecture 16: Adversarial Examples and Adversarial Training (CS231n)

1:21:46

Lecture 14: Deep Reinforcement Learning (CS231n)

Fei-Fei Li, Justin Johnson & Serena YeungStanford University

Professor

1:04:01·2017·graduate level

This session formalizes reinforcement learning using Markov Decision Processes, policies, and value functions. It discusses key algorithms such as Q-Learning, policy gradients, and Actor-Critic methods, illustrating applications in games like Atari and AlphaGo.

AI & MLReinforcement LearningDeep LearningAlgorithms & Data StructuresMarkov Decision Processes

In Lecture 14 we move from supervised learning to reinforcement learning (RL), in which an agent must learn to interact with an environment in order to maximize its reward. We formalize reinforcement learning using the language of Markov Decision Processes (MDPs), policies, value functions, and Q-Value functions. We discuss different algorithms for reinforcement learning including Q-Learning, policy gradients, and Actor-Critic. We show how deep reinforcement learning has been used to play Atari games and to achieve super-human Go performance in AlphaGo.

Keywords: Reinforcement learning, RL, Markov decision process, MDP, Q-Learning, policy gradients, REINFORCE, actor-critic, Atari games, AlphaGo

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Lecture 13: Generative Models (CS231n)

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Lecture 15: Efficient Methods and Hardware for Deep Learning (CS231n)

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