Deep Learning(CS7015): Lec 8.7 Adding Noise to the inputs

preview_player
Показать описание
lec08mod07
  1. NPTEL-NOC IITM
  2. deep learning
  3. prof.mitesh khapra
  4. iitmadras
  5. nptel deep learning
Рекомендации по теме
Deep Learning(CS7015): Lec 0:07:57

Deep Learning(CS7015): Lec 1.3 The Deep Revival

Deep Learning(CS7015): Lec 0:08:39

Deep Learning(CS7015): Lec 7.6 Contractive Autoencoders

Deep Learning Part 0:47:55

Deep Learning Part - II (CS7015): Lec 19.3 Setting up a Markov Chain for RBMs

Deep Learning(CS7015): Lec 0:28:09

Deep Learning(CS7015): Lec 9.3 Better activation functions

Deep Learning(CS7015): Lec 0:27:38

Deep Learning(CS7015): Lec 15.3 Attention Mechanism

Deep Learning(CS7015): Lec 0:08:10

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

Deep Learning(CS7015): Lec 0:11:59

Deep Learning(CS7015): Lec 5.5 Nesterov Accelerated Gradient Descent

Deep Learning(CS7015): Lec 0:22:50

Deep Learning(CS7015): Lec 11.5 Image Classification continued (GoogLeNet and ResNet)

Deep Learning(CS7015): Lec 0:15:44

Deep Learning(CS7015): Lec 9.5 Batch Normalization

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

Deep Learning(CS7015): Lec 11.3 Convolutional Neural Networks

Deep Learning(CS7015): Lec 0:07:05

Deep Learning(CS7015): Lec 10.6 Contrastive estimation

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

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

Deep Learning Part 0:15:24

Deep Learning Part - II (CS7015): Lec 17.3 Local Independencies in a Markov Network

Deep Learning(CS7015): Lec 0:25:42

Deep Learning(CS7015): Lec 6.4 Principal Component Analysis and its Interpretations

Deep Learning(CS7015): Lec 0:23:53

Deep Learning(CS7015): Lec 14.3 (Part-2) How LSTMs avoid the problem of vanishing gradients (Contd.)

Deep Learning(CS7015): Lec 0:02:57

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

Deep Learning Part 0:17:07

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

Convolutional Block Attention 0:07:05

Convolutional Block Attention Module (CBAM) Paper Explained

12. Perceptron Learning 0:10:14

12. Perceptron Learning Rule to classify given example Solve example Soft computing by Mahesh Huddar

Perceptron 0:13:22

Perceptron

Introduction to Deep 0:10:05

Introduction to Deep Learning - Module 1 - Video 7: Sigmoidal Output Units

Tutorial 7 - 1:07:04

Tutorial 7 - Attention | Deep Learning on Computational Accelerators

Deeplearning implementation in 1:34:01

Deeplearning implementation in Keras/Tensorflow for CNN/GAN/Autoencoder

CS480/680 Lecture 16: 1:15:18

CS480/680 Lecture 16: Convolutional neural networks