NOC:Deep Learning (USB)

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106106184



Media Storage Type : 64 GB USB Stick

NPTEL Subject Matter Expert : Prof.Mitesh Khapra

NPTEL Co-ordinating Institute : IIT Madras

NPTEL Lecture Count : 117

NPTEL Course Size : 41 GB

NPTEL PDF Text Transcription : Available and Included

NPTEL Subtitle Transcription : Available and Included (SRT)


Lecture Titles:

Lecture 1 - Biological Neuron
Lecture 2 - From Spring to Winter of AI
Lecture 3 - The Deep Revival
Lecture 4 - From Cats to Convolutional Neural Networks
Lecture 5 - Faster, higher, stronger
Lecture 6 - The Curious Case of Sequences
Lecture 7 - Beating humans at their own games (literally)
Lecture 8 - The Madness (2013)
Lecture 9 - (Need for) Sanity
Lecture 10 - Motivation from Biological Neurons
Lecture 11 - McCulloch Pitts Neuron, Thresholding Logic
Lecture 12 - Perceptrons
Lecture 13 - Error and Error Surfaces
Lecture 14 - Perceptron Learning Algorithm
Lecture 15 - Proof of Convergence of Perceptron Learning Algorithm
Lecture 16 - Deep Learning (CS7015): Linearly Separable Boolean Functions
Lecture 17 - Deep Learning (CS7015): Representation Power of a Network of Perceptrons
Lecture 18 - Deep Learning (CS7015): Sigmoid Neuron
Lecture 19 - Deep Learning (CS7015): A typical Supervised Machine Learning Setup
Lecture 20 - Deep Learning (CS7015): Learning Parameters: (Infeasible) guess work
Lecture 21 - Deep Learning (CS7015): Learning Parameters: Gradient Descent
Lecture 22 - Deep Learning (CS7015): Representation Power of Multilayer Network of Sigmoid Neurons
Lecture 23 - Feedforward Neural Networks (a.k.a multilayered network of neurons)
Lecture 24 - Learning Paramters of Feedforward Neural Networks (Intuition)
Lecture 25 - Output functions and Loss functions
Lecture 26 - Backpropagation (Intuition)
Lecture 27 - Backpropagation: Computing Gradients w.r.t. the Output Units
Lecture 28 - Backpropagation: Computing Gradients w.r.t. Hidden Units
Lecture 29 - Backpropagation: Computing Gradients w.r.t. Parameters
Lecture 30 - Backpropagation: Pseudo code
Lecture 31 - Derivative of the activation function
Lecture 32 - Information content, Entropy and cross entropy
Lecture 33 - Recap: Learning Parameters: Guess Work, Gradient Descent
Lecture 34 - Contours Maps
Lecture 35 - Momentum based Gradient Descent
Lecture 36 - Nesterov Accelerated Gradient Descent
Lecture 37 - Stochastic And Mini-Batch Gradient Descent
Lecture 38 - Tips for Adjusting Learning Rate and Momentum
Lecture 39 - Line Search
Lecture 40 - Gradient Descent with Adaptive Learning Rate
Lecture 41 - Bias Correction in Adam
Lecture 42 - Eigenvalues and Eigenvectors
Lecture 43 - Linear Algebra : Basic Definitions
Lecture 44 - Eigenvalue Decompositon
Lecture 45 - Principal Component Analysis and its Interpretations
Lecture 46 - PCA: Interpretation 2
Lecture 47 - PCA: Interpretation 3
Lecture 48 - PCA: Interpretation 3 (Continued...)
Lecture 49 - PCA: Practical Example
Lecture 50 - Singular Value Decomposition
Lecture 51 - Introduction to Autoncoders
Lecture 52 - Link between PCA and Autoencoders
Lecture 53 - Regularization in autoencoders (Motivation)
Lecture 54 - Denoising Autoencoders
Lecture 55 - Sparse Autoencoders
Lecture 56 - Contractive Autoencoders
Lecture 57 - Bias and Variance
Lecture 58 - Train error vs Test error
Lecture 59 - Train error vs Test error (Recap)
Lecture 60 - True error and Model complexity
Lecture 61 - L2 regularization
Lecture 62 - Dataset augmentation
Lecture 63 - Parameter sharing and tying
Lecture 64 - Adding Noise to the inputs
Lecture 65 - Adding Noise to the outputs
Lecture 66 - Early stopping
Lecture 67 - Ensemble Methods
Lecture 68 - Dropout
Lecture 69 - A quick recap of training deep neural networks
Lecture 70 - Unsupervised pre-training
Lecture 71 - Better activation functions
Lecture 72 - Better initialization strategies
Lecture 73 - Batch Normalization
Lecture 74 - One-hot representations of words
Lecture 75 - Distributed Representations of words
Lecture 76 - SVD for learning word representations
Lecture 77 - SVD for learning word representations (Continued...)
Lecture 78 - Continuous bag of words model
Lecture 79 - Skip-gram model
Lecture 80 - Skip-gram model (Continued...)
Lecture 81 - Contrastive estimation
Lecture 82 - Hierarchical softmax
Lecture 83 - GloVe representations
Lecture 84 - Evaluating word representations
Lecture 85 - Relation between SVD and Word2Vec
Lecture 86 - The convolution operation
Lecture 87 - Relation between input size, output size and filter size
Lecture 88 - Convolutional Neural Networks
Lecture 89 - Convolutional Neural Networks (Continued...)
Lecture 90 - CNNs (success stories on ImageNet)
Lecture 91 - CNNs (success stories on ImageNet) (Continued...)
Lecture 92 - Image Classification continued (GoogLeNet and ResNet)
Lecture 93 - Visualizing patches which maximally activate a neuron
Lecture 94 - Visualizing filters of a CNN
Lecture 95 - Occlusion experiments
Lecture 96 - Finding influence of input pixels using backpropagation
Lecture 97 - Guided Backpropagation
Lecture 98 - Optimization over images
Lecture 99 - Create images from embeddings
Lecture 100 - Deep Dream
Lecture 101 - Deep Art
Lecture 102 - Fooling Deep Convolutional Neural Networks
Lecture 103 - Sequence Learning Problems
Lecture 104 - Recurrent Neural Networks
Lecture 105 - Backpropagation through time
Lecture 106 - The problem of Exploding and Vanishing Gradients
Lecture 107 - Some Gory Details
Lecture 108 - Selective Read, Selective Write, Selective Forget - The Whiteboard Analogy
Lecture 109 - Long Short Term Memory (LSTM) and Gated Recurrent Units (GRUs)
Lecture 110 - How LSTMs avoid the problem of vanishing gradients
Lecture 111 - How LSTMs avoid the problem of vanishing gradients (Continued...)
Lecture 112 - Introduction to Encoder Decoder Models
Lecture 113 - Applications of Encoder Decoder models
Lecture 114 - Attention Mechanism
Lecture 115 - Attention Mechanism (Continued...)
Lecture 116 - Attention over images
Lecture 117 - Hierarchical Attention

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