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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 10: Recurrent Neural Networks (CS231n)

Fei-Fei Li, Justin Johnson & Serena YeungStanford University

Professor

1:13:09·2017·graduate level

This session explores recurrent neural networks (RNNs) for sequence modeling, covering architectures like LSTM and GRU. It demonstrates their application in language modeling, image captioning, and the integration of soft spatial attention mechanisms.

AI & MLDeep LearningLanguage Models & NLPRecurrent Neural NetworksLSTM

In Lecture 10 we discuss the use of recurrent neural networks for modeling sequence data. We show how recurrent neural networks can be used for language modeling and image captioning, and how soft spatial attention can be incorporated into image captioning models. We discuss different architectures for recurrent neural networks, including Long Short Term Memory (LSTM) and Gated Recurrent Units (GRU).

Keywords: Recurrent neural networks, RNN, language modeling, image captioning, soft attention, LSTM, GRU

Previous · Lecture 9

Lecture 9: CNN Architectures (CS231n)

Next · Lecture 11

Lecture 11: Detection and Segmentation (CS231n)

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