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NOC:Deep Learning
broucher Course Brochure
NOC:Deep Learning
by Prof Mitesh Khapra

Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on artificial neural networks. Learning can be supervised, semi-supervised or unsupervised.Deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks and convolu...

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Program Fee
FREE
About This Course

Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on artificial neural networks. Learning can be supervised, semi-supervised or unsupervised.Deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks and convolu...

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Course Curriculum
11 Sections | 98 Lecture | 30h 49min total length
Expand all sections
NOC:Deep Learning

Week 1

| 03h 54min total length
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    Proof of Convergence of Perceptron Learning Algorithm

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    Perceptron Learning Algorithm

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    Error and Error Surfaces

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    Perceptrons

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    McCulloch Pitts Neuron, Thresholding Logic

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    Motivation from Biological Neurons

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    (Need for) Sanity

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    The Madness (2013-)

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    Beating humans at their own games (literally)

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    The Curious Case of Sequences

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    Faster, higher, stronger

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    From Cats to Convolutional Neural Networks

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    The Deep Revival

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    From Spring to Winter of AI

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    Biological Neuron

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NOC:Deep Learning

Week 2

| 04h 09min total length
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    Deep Learning(CS7015): Learning Parameters: Gradient Descent

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    Deep Learning(CS7015): Learning Parameters: (Infeasible) guess work

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    Deep Learning(CS7015): A typical Supervised Machine Learning Setup

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    Deep Learning(CS7015): Sigmoid Neuron

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    Deep Learning(CS7015): Linearly Separable Boolean Functions

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    Deep Learning(CS7015): Representation Power of a Network of Perceptrons

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    Deep Learning(CS7015) :Representation Power of Multilayer Network

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NOC:Deep Learning

Week 3

| 07h 24min total length
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    Information content, Entropy & cross entropy

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    Derivative of the activation function

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    Backpropagation: Pseudo code

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    Backpropagation: Computing Gradients w.r.t. Parameters

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    Backpropagation: Computing Gradients w.r.t. Hidden Units

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    Backpropagation: Computing Gradients w.r.t. the Output Units

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    Backpropagation (Intuition)

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    Output functions and Loss functions

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    Learning Paramters of Feedforward Neural Networks (Intuition)

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    Feedforward Neural Networks (a.k.a multilayered network of neurons)

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NOC:Deep Learning

Week 4

| 01h 31min total length
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    Gradient Descent with Adaptive Learning Rate

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    Line Search

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    Tips for Adjusting Learning Rate and Momentum

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    Stochastic And Mini-Batch Gradient Descent

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    Nesterov Accelerated Gradient Descent

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    Bias Correction in Adam

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    Momentum based Gradient Descent

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    Contours Maps

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    Recap: Learning Parameters: Guess Work, Gradient Descent

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NOC:Deep Learning

Week 5

| 03h 36min total length
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    Eigenvalue Decompositon

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    Principal Component Analysis and its Interpretations

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    Linear Algebra : Basic Definitions

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    Eigenvalues and Eigenvectors

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    Singular Value Decomposition

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    PCA : Practical Example

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    PCA : Interpretation 3

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    PCA : Interpretation 3 (Contd.)

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    PCA : Interpretation 2

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NOC:Deep Learning

Week 6

| 51min 54sec total length
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    Contractive Autoencoders

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    Sparse Autoencoders

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    Denoising Autoencoders

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    Regularization in autoencoders (Motivation)

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    Link between PCA and Autoencoders

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    Introduction to Autoncoders

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NOC:Deep Learning

Week 7

| 49min 51sec total length
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    Dropout

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    Ensemble Methods

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    Early stopping

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NOC:Deep Learning

Week 8

| 01h 41min total length
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    A quick recap of training deep neural networks

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    Unsupervised pre-training

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    Better activation functions

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    Better initialization strategies

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    Batch Normalization

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NOC:Deep Learning

week 9

| 02h 15min total length
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    One-hot representations of words

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    Distributed Representations of words

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    SVD for learning word representations

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    SVD for learning word representations (Contd.)

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    Continuous bag of words model

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    Skip-gram model

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    Skip-gram model (Contd.)

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    Contrastive estimation

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    Hierarchical softmax

    13m <?php echo $alt; ?>
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    GloVe representations

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    Evaluating word representations

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    Relation between SVD and Word2Vec

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NOC:Deep Learning

Week 10

| 02h 51min total length
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    The convolution operation

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    Relation between input size, output size and filter size

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    Convolutional Neural Networks

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    Convolutional Neural Networks (Contd.)

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    CNNs (success stories on ImageNet)

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    Image Classification continued (GoogLeNet and ResNet)

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    Visualizing patches which maximally activate a neuron

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    Visualizing filters of a CNN

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    Occlusion experiments

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    Finding influence of input pixels using backpropagation

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    Guided Backpropagation

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    Optimization over images

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    Deep Dream

    11m <?php echo $alt; ?>
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    Create images from embeddings

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    Deep Art

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    Fooling Deep Convolutional Neural Networks

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NOC:Deep Learning

Week 11

| 01h 41min total length
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    Introduction to Encoder Decoder Models

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    Applications of Encoder Decoder models

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    Attention Mechanism

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    Attention Mechanism (Contd.)

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    Attention over images

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    Hierarchical Attention

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show 8 more sections
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