【课程】斯坦福 CS231n: 卷积神经网络 (2016 冬 + 2017 春合集 | 英字)

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视频转自: https://www.youtube.com/playlist?list=PLkt2uSq6rBVctENoVBg1TpCC7OQi31AlC https://www.youtube.com/playlist?list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv 课程主页:http://cs231n.stanford.edu 经典入门课程,做个合集
视频选集
(7/31)
CS231n Winter 2016- Lecture 1- Introduction and Historical Context
01:19:08
CS231n Winter 2016- Lecture 2- Data-driven approach, kNN, Linear Classification
57:29
CS231n Winter 2016- Lecture 3- Linear Classification 2, Optimization
01:11:23
CS231n Winter 2016- Lecture 4- Backpropagation, Neural Networks 1
01:19:39
CS231n Winter 2016- Lecture 5- Neural Networks Part 2
01:18:38
CS231n Winter 2016- Lecture 6- Neural Networks Part 3 - Intro to ConvNets
01:09:36
CS231n Winter 2016- Lecture 7- Convolutional Neural Networks
01:19:01
CS231n Winter 2016- Lecture 8- Localization and Detection
01:04:58
CS231n Winter 2016- Lecture 9- Visualization, Deep Dream, Neural Style, Adversar
01:18:20
CS231n Winter 2016- Lecture 10- Recurrent Neural Networks, Image Captioning, LST
01:09:54
CS231n Winter 2016- Lecture 11- ConvNets in practice
01:15:04
CS231n Winter 2016- Lecture 12- Deep Learning libraries
01:21:07
CS231n Winter 2016- Lecture 13- Segmentation, soft attention, spatial transforme
01:11:00
CS231n Winter 2016- Lecture 14- Videos and Unsupervised Learning
01:17:36
CS231n Winter 2016- Lecture 15- Invited Talk by Jeff Dean
01:14:50
Lecture 1 | Introduction to Convolutional Neural Networks for Visual Recognition
57:57
Lecture 2 | Image Classification
59:32
Lecture 3 | Loss Functions and Optimization
01:14:41
Lecture 4 | Introduction to Neural Networks
01:13:59
Lecture 5 | Convolutional Neural Networks
01:08:57
Lecture 6 | Training Neural Networks I
01:20:20
Lecture 7 | Training Neural Networks II
01:15:30
Lecture 8 | Deep Learning Software
01:18:08
Lecture 9 | CNN Architectures
01:17:40
Lecture 10 | Recurrent Neural Networks
01:13:09
Lecture 11 | Detection and Segmentation
01:14:27
Lecture 12 | Visualizing and Understanding
01:15:48
Lecture 13 | Generative Models
01:17:42
Lecture 14 | Deep Reinforcement Learning
01:04:01
Lecture 15 | Efficient Methods and Hardware for Deep Learning
01:16:53
Lecture 16 | Adversarial Examples and Adversarial Training
01:21:46
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