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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 5 | Convolutional Neural Networks

Fei-Fei Li, Justin Johnson & Serena YeungStanford University

Stanford Vision & Learning Lab Faculty

1:08:56·2017·graduate level

This lecture introduces the foundational concepts of Convolutional Neural Networks (CNNs), tracing their historical evolution from early models like the Perceptron and Neocognitron to influential architectures such as LeNet and AlexNet. Key components including convolution, pooling, and fully-connected layers are formally defined.

AI & MLDeep LearningBehavioral & Evolutionary BiologyComputer VisionNeural Network Architectures

In Lecture 5 we move from fully-connected neural networks to convolutional neural networks. We discuss some of the key historical milestones in the development of convolutional networks, including the perceptron, the neocognitron, LeNet, and AlexNet. We introduce convolution, pooling, and fully-connected layers which form the basis for modern convolutional networks.

Keywords: Convolutional neural networks, perceptron, neocognitron, LeNet, AlexNet, convolution, pooling, fully-connected layers

Previous · Lecture 4

Lecture 4 | Introduction to Neural Networks

Next · Lecture 6

Lecture 6 | Training Neural Networks I

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