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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 4 | Introduction to Neural Networks

Fei-Fei Li, Justin Johnson & Serena YeungStanford University

Stanford Vision & Learning Lab Faculty

1:13:59·2017·graduate level

This lecture transitions from linear classifiers to fully-connected neural networks, detailing the backpropagation algorithm for gradient computation. It briefly explores connections between artificial and biological neural networks.

AI & MLDeep LearningAlgorithms & Data StructuresNeural NetworksBackpropagation

In Lecture 4 we progress from linear classifiers to fully-connected neural networks. We introduce the backpropagation algorithm for computing gradients and briefly discuss connections between artificial neural networks and biological neural networks.

Keywords: Neural networks, computational graphs, backpropagation, activation functions, biological neurons

Previous · Lecture 3

Lecture 3 | Loss Functions and Optimization

Next · Lecture 5

Lecture 5 | Convolutional Neural Networks

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