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Deep Learning(CS7015): Lec 8.3 True error and Model complexity
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Deep Learning(CS7015): Lec 8.3 True error and Model complexity
Deep Learning(CS7015): Lec 2.8 Representation Power of a Network of Perceptrons
Deep Learning(CS7015): Lec 4.3 Output functions and Loss functions
Deep Learning(CS7015): Lec 8.1 Bias and Variance
Deep Learning(CS7015): Lec 2.3 Perceptrons
Deep Learning(CS7015): Lec 5.4 Momentum based Gradient Descent
Deep Learning(CS7015): Lec 1.2 From Spring to Winter of AI
Deep Learning(CS7015): Lec 8.8 Adding Noise to the outputs
Deep Learning(CS7015): Lec 1.3 The Deep Revival
Deep Learning(CS7015): Lec 9.1 A quick recap of training deep neural networks
Deep Learning(CS7015): Lec 6.4 Principal Component Analysis and its Interpretations
Deep Learning(CS7015): Lec 15.3 Attention Mechanism
138 - The need for scaling, dropout, and batch normalization in deep learning
Deep Learning(CS7015): Lec 9.4 Better initialization strategies
Deep Learning Part - II (CS7015): Lec 19.3 Setting up a Markov Chain for RBMs
Deep Learning(CS7015): Lec 8.7 Adding Noise to the inputs
Deep Learning Part - II (CS7015): Lec 16.1 Why are we interested in Joint Distributions
Deep Learning(CS7015): Lec 15.4 Attention over images
Activation Function - Feedforward neural networks (deep learning)
Deep Learning(CS7015): Lec 2.2 McCulloch Pitts Neuron, Thresholding Logic
L19/1 Recurrent Neural Networks
Deep Learning(CS7015): Lec 10.2 Distributed Representations of words
Deep Learning(CS7015): Lec 13.4 The problem of Exploding and Vanishing Gradients
Lecture 8 | Normalization, Regularization etc.
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