NOC:Deep Learning - Study24x7
NOC:Deep Learning

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NOC:Deep Learning

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Description

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 convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases superior to human experts.

Includes

  • NOC:Deep Learning Total 11 Modules
  • NOC:Deep Learning 98 Videos
  • NOC:Deep Learning Downloadable on mobile
  • NOC:Deep Learning Published on 05 July, 2019

Course Content

Week 1

NOC:Deep Learning
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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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Week 2

NOC:Deep Learning
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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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Week 3

NOC:Deep Learning
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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

    44m <?php echo  $alt; ?>
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    Backpropagation: Computing Gradients w.r.t. the Output Units

    44m <?php echo  $alt; ?>
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    Backpropagation (Intuition)

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    Output functions and Loss functions

    44m <?php echo  $alt; ?>
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    Learning Paramters of Feedforward Neural Networks (Intuition)

    44m <?php echo  $alt; ?>
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    Feedforward Neural Networks (a.k.a multilayered network of neurons)

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Week 4

NOC:Deep Learning
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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

    10m <?php echo  $alt; ?>
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    Nesterov Accelerated Gradient Descent

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    Bias Correction in Adam

    10m <?php echo  $alt; ?>
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    Momentum based Gradient Descent

    10m <?php echo  $alt; ?>
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    Contours Maps

    10m <?php echo  $alt; ?>
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    Recap: Learning Parameters: Guess Work, Gradient Descent

    10m <?php echo  $alt; ?>

Week 5

NOC:Deep Learning
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    Eigenvalue Decompositon

    9m <?php echo  $alt; ?>
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    Principal Component Analysis and its Interpretations

    25m <?php echo  $alt; ?>
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    Linear Algebra : Basic Definitions

    25m <?php echo  $alt; ?>
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    Eigenvalues and Eigenvectors

    25m <?php echo  $alt; ?>
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    Singular Value Decomposition

    26m <?php echo  $alt; ?>
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    PCA : Practical Example

    26m <?php echo  $alt; ?>
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    PCA : Interpretation 3

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    PCA : Interpretation 3 (Contd.)

    26m <?php echo  $alt; ?>
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    PCA : Interpretation 2

    26m <?php echo  $alt; ?>

Week 6

NOC:Deep Learning
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    Contractive Autoencoders

    8m <?php echo  $alt; ?>
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    Sparse Autoencoders

    8m <?php echo  $alt; ?>
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    Denoising Autoencoders

    8m <?php echo  $alt; ?>
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    Regularization in autoencoders (Motivation)

    8m <?php echo  $alt; ?>
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    Link between PCA and Autoencoders

    8m <?php echo  $alt; ?>
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    Introduction to Autoncoders

    8m <?php echo  $alt; ?>

Week 7

NOC:Deep Learning
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    Dropout

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    Ensemble Methods

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    Early stopping

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Week 8

NOC:Deep Learning
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    A quick recap of training deep neural networks

    6m <?php echo  $alt; ?>
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    Unsupervised pre-training

    24m <?php echo  $alt; ?>
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    Better activation functions

    28m <?php echo  $alt; ?>
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    Better initialization strategies

    26m <?php echo  $alt; ?>
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    Batch Normalization

    15m <?php echo  $alt; ?>

week 9

NOC:Deep Learning
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    One-hot representations of words

    9m <?php echo  $alt; ?>
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    Distributed Representations of words

    12m <?php echo  $alt; ?>
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    SVD for learning word representations

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    SVD for learning word representations (Contd.)

    1m <?php echo  $alt; ?>
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    Continuous bag of words model

    36m <?php echo  $alt; ?>
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    Skip-gram model

    10m <?php echo  $alt; ?>
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    Skip-gram model (Contd.)

    8m <?php echo  $alt; ?>
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    Contrastive estimation

    7m <?php echo  $alt; ?>
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    Hierarchical softmax

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

    7m <?php echo  $alt; ?>
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    Evaluating word representations

    8m <?php echo  $alt; ?>
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    Relation between SVD and Word2Vec

    4m <?php echo  $alt; ?>

Week 10

NOC:Deep Learning
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    The convolution operation

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    Relation between input size, output size and filter size

    12m <?php echo  $alt; ?>
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    Convolutional Neural Networks

    16m <?php echo  $alt; ?>
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    Convolutional Neural Networks (Contd.)

    17m <?php echo  $alt; ?>
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    CNNs (success stories on ImageNet)

    20m <?php echo  $alt; ?>
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    Image Classification continued (GoogLeNet and ResNet)

    22m <?php echo  $alt; ?>
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    Visualizing patches which maximally activate a neuron

    6m <?php echo  $alt; ?>
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    Visualizing filters of a CNN

    6m <?php echo  $alt; ?>
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    Occlusion experiments

    6m <?php echo  $alt; ?>
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    Finding influence of input pixels using backpropagation

    6m <?php echo  $alt; ?>
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    Guided Backpropagation

    4m <?php echo  $alt; ?>
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    Optimization over images

    10m <?php echo  $alt; ?>
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    Deep Dream

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

    11m <?php echo  $alt; ?>
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    Deep Art

    5m <?php echo  $alt; ?>
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    Fooling Deep Convolutional Neural Networks

    6m <?php echo  $alt; ?>

Week 11

NOC:Deep Learning
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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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