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Deep Learning(CS7015): Lec 3.2 A typical Supervised Machine Learning Setup
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Deep Learning
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- 1 Deep Learning - Course Introduction
- 2 Deep Learning(CS7015): Lec 1.1 Biological Neuron
- 3 Deep Learning(CS7015): Lec 1.2 From Spring to Winter of AI
- 4 Deep Learning(CS7015): Lec 1.3 The Deep Revival
- 5 Deep Learning(CS7015): Lec 1.4 From Cats to Convolutional Neural Networks
- 6 Deep Learning(CS7015): Lec 1.5 Faster, higher, stronger
- 7 Deep Learning(CS7015): Lec 1.6 The Curious Case of Sequences
- 8 Deep Learning(CS7015): Lec 1.7 Beating humans at their own games (literally)
- 9 Deep Learning(CS7015): Lec 1.8 The Madness (2013-)
- 10 Deep Learning(CS7015): Lec 1.9 (Need for) Sanity
- 11 Deep Learning(CS7015): Lec 2.1 Motivation from Biological Neurons
- 12 Deep Learning(CS7015): Lec 2.2 McCulloch Pitts Neuron, Thresholding Logic
- 13 Deep Learning(CS7015): Lec 2.3 Perceptrons
- 14 Deep Learning(CS7015): Lec 2.4 Error and Error Surfaces
- 15 Deep Learning(CS7015): Lec 2.5 Perceptron Learning Algorithm
- 16 Deep Learning(CS7015): Lec 2.6 Proof of Convergence of Perceptron Learning Algorithm
- 17 Deep Learning(CS7015): Lec 2.7 Linearly Separable Boolean Functions
- 18 Deep Learning(CS7015): Lec 2.8 Representation Power of a Network of Perceptrons
- 19 Deep Learning(CS7015): Lec 3.1 Sigmoid Neuron
- 20 Deep Learning(CS7015): Lec 3.2 A typical Supervised Machine Learning Setup
- 21 Deep Learning(CS7015): Lec 3.3 Learning Parameters: (Infeasible) guess work
- 22 Deep Learning(CS7015): Lec 3.4 Learning Parameters: Gradient Descent
- 23 Deep Learning(CS7015): Lec 3.5 Representation Power of Multilayer Network of Sigmoid Neurons
- 24 Deep Learning(CS7015): Lec 4.1 Feedforward Neural Networks (a.k.a multilayered network of neurons)
- 25 Deep Learning(CS7015): Lec 4.2 Learning Paramters of Feedforward Neural Networks (Intuition)
- 26 Deep Learning(CS7015): Lec 4.3 Output functions and Loss functions
- 27 Deep Learning(CS7015): Lec 4.4 Backpropagation (Intuition)
- 28 Deep Learning(CS7015): Lec 4.5 Backpropagation: Computing Gradients w.r.t. the Output Units
- 29 Deep Learning(CS7015): Lec 4.6 Backpropagation: Computing Gradients w.r.t. Hidden Units
- 30 Deep Learning(CS7015): Lec 4.7 Backpropagation: Computing Gradients w.r.t. Parameters
- 31 Deep Learning(CS7015): Lec 4.8 Backpropagation: Pseudo code
- 32 Deep Learning(CS7015): Lec 4.9 Derivative of the activation function
- 33 Deep Learning(CS7015): Lec 6.6 PCA : Interpretation 3
- 34 Deep Learning(CS7015): Lec 4.10 Information content, Entropy & cross entropy
- 35 Deep Learning(CS7015): Lec 5.1 & Lec 5.2 Recap: Learning Parameters: Guess Work, Gradient Descent
- 36 Deep Learning(CS7015): Lec 5.3 Contours Maps
- 37 Deep Learning(CS7015): Lec 5.4 Momentum based Gradient Descent
- 38 Deep Learning(CS7015): Lec 5.5 Nesterov Accelerated Gradient Descent
- 39 Deep Learning(CS7015): Lec 5.6 Stochastic And Mini-Batch Gradient Descent
- 40 Deep Learning(CS7015): Lec 5.7 Tips for Adjusting Learning Rate and Momentum
- 41 Deep Learning(CS7015): Lec 5.8 Line Search
- 42 Deep Learning(CS7015): Lec 5.9 Gradient Descent with Adaptive Learning Rate
- 43 Deep Learning(CS7015): Lec 5.9 (Part-2) Bias Correction in Adam
- 44 Deep Learning(CS7015): Lec 6.1 Eigenvalues and Eigenvectors
- 45 Deep Learning(CS7015): Lec 6.2 Linear Algebra : Basic Definitions
- 46 Deep Learning(CS7015): Lec 6.3 Eigenvalue Decompositon
- 47 Deep Learning(CS7015): Lec 6.4 Principal Component Analysis and its Interpretations
- 48 Deep Learning(CS7015): Lec 6.5 PCA : Interpretation 2
- 49 Deep Learning(CS7015): Lec 6.6 (Part-2) PCA : Interpretation 3 (Contd.)
- 50 Deep Learning(CS7015): Lec 6.7 PCA : Practical Example
- 51 Deep Learning(CS7015): Lec 6.8 Singular Value Decomposition
- 52 Deep Learning(CS7015): Lec 7.1 Introduction to Autoncoders
- 53 Deep Learning(CS7015): Lec 7.2 Link between PCA and Autoencoders
- 54 Deep Learning(CS7015): Lec 7.3 Regularization in autoencoders (Motivation)
- 55 Deep Learning(CS7015): Lec 7.4 Denoising Autoencoders
- 56 Deep Learning(CS7015): Lec 7.5 Sparse Autoencoders
- 57 Deep Learning(CS7015): Lec 7.6 Contractive Autoencoders
- 58 Deep Learning(CS7015): Lec 8.1 Bias and Variance
- 59 Deep Learning(CS7015): Lec 8.2 Train error vs Test error
- 60 Deep Learning(CS7015): Lec 8.2 (Part-2) Train error vs Test error (Recap)
- 61 Deep Learning(CS7015): Lec 8.3 True error and Model complexity
- 62 Deep Learning(CS7015): Lec 8.4 L2 regularization
- 63 Deep Learning(CS7015): Lec 8.5 Dataset augmentation
- 64 Deep Learning(CS7015): Lec 8.6 Parameter sharing and tying
- 65 Deep Learning(CS7015): Lec 8.7 Adding Noise to the inputs
- 66 Deep Learning(CS7015): Lec 8.8 Adding Noise to the outputs
- 67 Deep Learning(CS7015): Lec 8.9 Early stopping
- 68 Deep Learning(CS7015): Lec 8.10 Ensemble Methods
- 69 Deep Learning(CS7015): Lec 8.11 Dropout
- 70 Deep Learning(CS7015): Lec 9.1 A quick recap of training deep neural networks
- 71 Deep Learning(CS7015): Lec 9.2 Unsupervised pre-training
- 72 Deep Learning(CS7015): Lec 9.3 Better activation functions
- 73 Deep Learning(CS7015): Lec 9.4 Better initialization strategies
- 74 Deep Learning(CS7015): Lec 9.5 Batch Normalization
- 75 Deep Learning(CS7015): Lec 10.1 One-hot representations of words
- 76 Deep Learning(CS7015): Lec 10.2 Distributed Representations of words
- 77 Deep Learning(CS7015): Lec 10.3 SVD for learning word representations
- 78 Deep Learning(CS7015): Lec 10.3 (Part-2) SVD for learning word representations (Contd.)
- 79 Deep Learning(CS7015): Lec 10.4 Continuous bag of words model
- 80 Deep Learning(CS7015): Lec 10.5 Skip-gram model
- 81 Deep Learning(CS7015): Lec 10.5 (Part-2) Skip-gram model (Contd.)
- 82 Deep Learning(CS7015): Lec 10.6 Contrastive estimation
- 83 Deep Learning(CS7015): Lec 10.7 Hierarchical softmax
- 84 Deep Learning(CS7015): Lec 10.8 GloVe representations
- 85 Deep Learning(CS7015): Lec 10.9 Evaluating word representations
- 86 Deep Learning(CS7015): Lec 10.10 Relation between SVD and Word2Vec
- 87 Deep Learning(CS7015): Lec 11.1 The convolution operation
- 88 Deep Learning(CS7015): Lec 11.2 Relation between input size, output size and filter size
- 89 Deep Learning(CS7015): Lec 11.3 Convolutional Neural Networks
- 90 Deep Learning(CS7015): Lec 11.3 (Part-2) Convolutional Neural Networks (Contd.)
- 91 Deep Learning(CS7015): Lec 11.4 CNNs (success stories on ImageNet)
- 92 Deep Learning(CS7015): Lec 11.4 (Par-2) CNNs (success stories on ImageNet) (Contd.)
- 93 Deep Learning(CS7015): Lec 11.5 Image Classification continued (GoogLeNet and ResNet)
- 94 Deep Learning(CS7015): Lec 12.1 Visualizing patches which maximally activate a neuron
- 95 Deep Learning(CS7015): Lec 12.2 Visualizing filters of a CNN
- 96 Deep Learning(CS7015): Lec 12.3 Occlusion experiments
- 97 Deep Learning(CS7015): Lec 12.4 Finding influence of input pixels using backpropagation
- 98 Deep Learning(CS7015): Lec 12.5 Guided Backpropagation
- 99 Deep Learning(CS7015): Lec 12.6 Optimization over images
- 100 Deep Learning(CS7015): Lec 12.7 Create images from embeddings
- 101 Deep Learning(CS7015): Lec 12.8 Deep Dream
- 102 Deep Learning(CS7015): Lec 12.9 Deep Art
- 103 Deep Learning(CS7015): Lec 12.10 Fooling Deep Convolutional Neural Networks
- 104 Deep Learning(CS7015): Lec 13.1 Sequence Learning Problems
- 105 Deep Learning(CS7015): Lec 13.2 Recurrent Neural Networks
- 106 Deep Learning(CS7015): Lec 13.3 Backpropagation through time
- 107 Deep Learning(CS7015): Lec 13.4 The problem of Exploding and Vanishing Gradients
- 108 Deep Learning(CS7015): Lec 13.5 Some Gory Details
- 109 Deep Learning(CS7015): Lec 14.1 Selective Read, Selective Write, Selective Forget
- 110 Deep Learning(CS7015): Lec 14.2 Long Short Term Memory(LSTM) and Gated Recurrent Units(GRUs)
- 111 Deep Learning(CS7015): Lec 14.3 How LSTMs avoid the problem of vanishing gradients
- 112 Deep Learning(CS7015): Lec 14.3 (Part-2) How LSTMs avoid the problem of vanishing gradients (Contd.)
- 113 Deep Learning(CS7015): Lec 15.1 Introduction to Encoder Decoder Models
- 114 Deep Learning(CS7015): Lec 15.2 Applications of Encoder Decoder models
- 115 Deep Learning(CS7015): Lec 15.3 Attention Mechanism
- 116 Deep Learning(CS7015): Lec 15.3 (Part-2) Attention Mechanism (Contd.)
- 117 Deep Learning(CS7015): Lec 15.4 Attention over images
- 118 Deep Learning(CS7015): Lec 15.5 Hierarchical Attention