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Deep Learning(CS7015): Lec 13.5 Some Gory Details
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lec13mod05
Deep Learning(CS7015): Lec 13.1 Sequence Learning Problems
Deep Learning(CS7015): Lec 1.4 From Cats to Convolutional Neural Networks
Deep Learning(CS7015): Lec 5.7 Tips for Adjusting Learning Rate and Momentum
Deep Learning(CS7015): Lec 13.2 Recurrent Neural Networks
Deep Learning(CS7015): Lec 9.5 Batch Normalization
Deep Learning(CS7015): Lec 14.1 Selective Read, Selective Write, Selective Forget
Deep Learning(CS7015): Lec 10.4 Continuous bag of words model
Deep Learning(CS7015): Lec 13.3 Backpropagation through time
Deep Learning(CS7015): Lec 3.4 Learning Parameters: Gradient Descent
Deep Learning(CS7015): Lec 15.1 Introduction to Encoder Decoder Models
Early Stopping & Dropout: Ways to overcome Overfitting
Deep Learning(CS7015): Lec 5.4 Momentum based Gradient Descent
Deep Learning Part - II (CS7015): Lec 16.9 Bayesian Networks : Formal Semantics
Deep Learning(CS7015): Lec 15.4 Attention over images
Deep Learning(CS7015): Lec 1.2 From Spring to Winter of AI
Deep Learning(CS7015): Lec 4.7 Backpropagation: Computing Gradients w.r.t. Parameters
Deep Learning(CS7015): Lec 2.2 McCulloch Pitts Neuron, Thresholding Logic
Deep Learning Part - II (CS7015): Lec 18.7 Motivation for Sampling
Deep Learning Part - II (CS7015): Lec 16.3 Can we represent the joint distribution more compactly
Deep Learning(CS7015): Lec 2.6 Proof of Convergence of Perceptron Learning Algorithm
Deep Learning(CS7015): Lec 8.10 Ensemble Methods
Deep Learning(CS7015): Lec 10.2 Distributed Representations of words
Deep Learning(CS7015): Lec 10.7 Hierarchical softmax
Deep Learning(CS7015): Lec 8.6 Parameter sharing and tying
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