NOC:Deep Learning - Part 2 - Study24x7
NOC:Deep Learning - Part 2

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NOC:Deep Learning - Part 2

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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 - Part 2 Total 8 Modules
  • NOC:Deep Learning - Part 2 37 Videos
  • NOC:Deep Learning - Part 2 Downloadable on mobile
  • NOC:Deep Learning - Part 2 Published on 05 July, 2019

Course Content

WEEK 1

NOC:Deep Learning - Part 2
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    Recap of Probability Theory

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    Why are we interested in Joint Distributions

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    How do we represent a joint distribution

    4m <?php echo  $alt; ?>
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    Can we represent the joint distribution more compactly

    15m <?php echo  $alt; ?>
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    Can we use a graph to represent a joint distribution

    17m <?php echo  $alt; ?>
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    Different types of reasoning encoded in a Bayesian Network

    16m <?php echo  $alt; ?>

Week 2

NOC:Deep Learning - Part 2
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    Independencies encoded by a Bayesian Network(Case 1: Node and it's parents)

    6m <?php echo  $alt; ?>
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    Independencies encoded by a Bayesian Network

    5m <?php echo  $alt; ?>
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    Independencies encoded by a Bayesian Network

    3m <?php echo  $alt; ?>
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    Bayesian Networks : Formal Semantics

    3m <?php echo  $alt; ?>
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    I-Maps

    12m <?php echo  $alt; ?>
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    Markov Networks: Motivation

    16m <?php echo  $alt; ?>
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    Factors in Markov Network

    21m <?php echo  $alt; ?>
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    Local Independencies in a Markov Network

    15m <?php echo  $alt; ?>

Week 3

NOC:Deep Learning - Part 2
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    Joint Distributions

    27m <?php echo  $alt; ?>
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    The concept of a latent variable

    27m <?php echo  $alt; ?>
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    Restricted Boltzmann Machines

    30m <?php echo  $alt; ?>
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    RBMs as Stochastic Neural Networks

    14m <?php echo  $alt; ?>
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    Unsupervised Learning with RBMs

    14m <?php echo  $alt; ?>

Week 4

NOC:Deep Learning - Part 2
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    Markov Chains

    10m <?php echo  $alt; ?>
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    Motivation for Sampling - Part - 02

    10m <?php echo  $alt; ?>
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    Why de we care about Markov Chains ?

    10m <?php echo  $alt; ?>
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    Motivation for Sampling

    10m <?php echo  $alt; ?>
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    Computing the gradient of the log likelihood

    10m <?php echo  $alt; ?>

Week 5

NOC:Deep Learning - Part 2
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    Setting up a Markov Chain for RBMs

    11m <?php echo  $alt; ?>
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    Training RBMs Using Gibbs Sampling

    11m <?php echo  $alt; ?>
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    Training RBMS Using Contrastive Divergence

    11m <?php echo  $alt; ?>

Week 6

NOC:Deep Learning - Part 2
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    Revisiting Autoencoders

    20m <?php echo  $alt; ?>
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    Variational Autoencoders: The Neural Network Perspective

    20m <?php echo  $alt; ?>
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    Variational Autoencoders: The Graphical model perspective

    46m <?php echo  $alt; ?>

Week 7

NOC:Deep Learning - Part 2
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    Neural Autoregressive Density Estimator

    46m <?php echo  $alt; ?>
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    Masked Autoencoder Density Estimator (MADE)

    35m <?php echo  $alt; ?>

Week 8

NOC:Deep Learning - Part 2
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    Generative Adversarial Networks - The Intuition

    25m <?php echo  $alt; ?>
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    Generative Adversarial Networks - Architecture

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    Generative Adversarial Networks - The Math Behind it

    22m <?php echo  $alt; ?>
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    Generative Adversarial Networks - Some Cool Stuff and Applications

    7m <?php echo  $alt; ?>
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    Bringing it all together (the deep generative summary)

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