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CS231n Winter 2016 Lecture 2 Data driven approach, kNN, Linear Classification 1-ZM4umP6F1Jc.mp4
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0:57:28
CS231n Winter 2016: Lecture 2: Data-driven approach, kNN, Linear Classification 1
0:57:28
CS231n Winter 2016 Lecture 2 Data driven approach, kNN, Linear Classification 1-ZM4umP6F1Jc.mp4
0:57:28
CS231n Winter 2016 Lecture 2 Data driven approach, kNN, Linear Classification 1-ZM4umP6F1Jc.mp4
1:11:23
CS231n Winter 2016: Lecture 3: Linear Classification 2, Optimization
0:55:40
CS231n Winter 2016 Lecture 2 Data driven approach, kNN, Linear Classification 1 8inugqHkfvE
1:18:38
CS231n Winter 2016: Lecture 5: Neural Networks Part 2
1:19:39
CS231n Winter 2016: Lecture 4: Backpropagation, Neural Networks 1
0:57:28
CS231n Lecture 2 - Data driven approach, kNN, Linear Classification 1
1:09:36
CS231n Winter 2016: Lecture 6: Neural Networks Part 3 / Intro to ConvNets
1:19:08
CS231n Winter 2016: Lecture1: Introduction and Historical Context
1:18:38
CS231n Winter 2016 Lecture 5 Neural Networks Part 2-jhUZ800C650.mp4
1:15:03
CS231n Winter 2016: Lecture 11: ConvNets in practice
1:09:54
CS231n Winter 2016: Lecture 10: Recurrent Neural Networks, Image Captioning, LSTM
1:18:38
CS231n Winter 2016 Lecture 5 Neural Networks Part 2
1:19:01
CS231n Winter 2016: Lecture 7: Convolutional Neural Networks
1:19:01
CS231n Winter 2016 Lecture 7 Convolutional Neural Networks LxfUGhug iQ-sHyIqu_S5Ks.mp4
1:21:07
CS231n Winter 2016: Lecture 12: Deep Learning libraries
1:17:36
CS231n Winter 2016: Lecture 14: Videos and Unsupervised Learning
1:04:57
CS231n Winter 2016: Lecture 8: Localization and Detection
1:18:20
CS231n Winter 2016: Lecture 9: Visualization, Deep Dream, Neural Style, Adversarial Examples
1:11:23
CS231n Winter 2016 Lecture 3 Linear Classification 2, Optimization
1:19:08
CS231n Winter 2016 Lecture 1 Introduction and Historical Context-F-g0-6_RRUA.mp4
1:14:50
CS231n Winter 2016: Lecture 15: Invited Talk by Jeff Dean
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CS231n Winter 2016 Lecture 3 Linear Classification 2, Optimization qlLChbHhbg4
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