Deep Learning(CS7015): Lec 11.4 (Par-2) CNNs (success stories on ImageNet) (Contd.)

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lec11mod04 part02
  1. NPTEL-NOC IITM
  2. deep learning
  3. prof.mitesh khapra
  4. iitmadras
  5. nptel deep learning
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Deep Learning(CS7015): Lec 0:02:57

Deep Learning(CS7015): Lec 11.4 (Par-2) CNNs (success stories on ImageNet) (Contd.)

Deep Learning(CS7015): Lec 0:16:58

Deep Learning(CS7015): Lec 11.3 Convolutional Neural Networks

Deep Learning(CS7015): Lec 0:20:49

Deep Learning(CS7015): Lec 11.4 CNNs (success stories on ImageNet)

Deep Learning(CS7015): Lec 0:12:27

Deep Learning(CS7015): Lec 11.2 Relation between input size, output size and filter size

Deep Learning Part 0:15:40

Deep Learning Part - II (CS7015): Lec 16.3 Can we represent the joint distribution more compactly

Deep Learning Part 0:11:58

Deep Learning Part - II (CS7015): Lec 18.6 Computing the gradient of the log likelihood

Deep Learning(CS7015): Lec 0:36:36

Deep Learning(CS7015): Lec 10.4 Continuous bag of words model

Deep Learning(CS7015): Lec 0:17:39

Deep Learning(CS7015): Lec 11.3 (Part-2) Convolutional Neural Networks (Contd.)

Deep Learning(CS7015): Lec 0:11:00

Deep Learning(CS7015): Lec 2.3 Perceptrons

Deep Learning(CS7015): Lec 0:13:52

Deep Learning(CS7015): Lec 4.4 Backpropagation (Intuition)

Deep Learning(CS7015): Lec 0:06:57

Deep Learning(CS7015): Lec 4.2 Learning Paramters of Feedforward Neural Networks (Intuition)

Deep Learning Part 0:17:07

Deep Learning Part - II (CS7015): Lec 16.4 Can we use a graph to represent a joint distribution

Deep Learning(CS7015): Lec 0:06:36

Deep Learning(CS7015): Lec 1.6 The Curious Case of Sequences

Neural Networks From 0:08:34

Neural Networks From Scratch - Lec 11 - Maxout Activation Function

Deep Learning(CS7015): Lec 0:11:31

Deep Learning(CS7015): Lec 15.4 Attention over images

Ali Ghodsi, Lec 0:46:05

Ali Ghodsi, Lec [2,2]: Deep Learning, Regularization

Deep Learning Part 0:34:40

Deep Learning Part - II (CS7015): Lec 19.1 Markov Chains

Deep Learning(CS7015): Lec 0:08:11

Deep Learning(CS7015): Lec 14.3 How LSTMs avoid the problem of vanishing gradients

Deep Learning(CS7015): Lec 0:02:03

Deep Learning(CS7015): Lec 1.7 Beating humans at their own games (literally)

Deep Learning Part 0:14:01

Deep Learning Part - II (CS7015): Lec 18.7 Motivation for Sampling

Deep Learning(CS7015): Lec 0:26:31

Deep Learning(CS7015): Lec 9.4 Better initialization strategies

Deep Learning Part 0:11:10

Deep Learning Part - II (CS7015): Lec 19.5 Training RBMS Using Contrastive Divergence

Deep Learning Part 0:22:30

Deep Learning Part - II (CS7015): Lec 22.3 Generative Adversarial Networks - The Math Behind it

Deep Learning Part 0:20:11

Deep Learning Part - II (CS7015): Lec 20.1 Revisiting Autoencoders

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Автор

I’m still searching for answer to the question “ how do we backpropogate through maxpool and ReLu layers…”

srinithyaravi