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NOC:Deep Learning - Part 2

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NOC:Deep Learning - Part 2

Validity Unlimited NOC:Deep Learning - Part 2 All Level NOC:Deep Learning - Part 2 English

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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 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.

  • 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 Curriculum

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

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    Can we represent the joint distribution more compactly

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    Can we use a graph to represent a joint distribution

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    Different types of reasoning encoded in a Bayesian Network

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Week 2

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

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    Independencies encoded by a Bayesian Network

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    Independencies encoded by a Bayesian Network

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    Bayesian Networks : Formal Semantics

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    I-Maps

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    Markov Networks: Motivation

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    Factors in Markov Network

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    Local Independencies in a Markov Network

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Week 3

NOC:Deep Learning - Part 2
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    Joint Distributions

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    The concept of a latent variable

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    Restricted Boltzmann Machines

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    RBMs as Stochastic Neural Networks

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    Unsupervised Learning with RBMs

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Week 4

NOC:Deep Learning - Part 2
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    Markov Chains

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    Motivation for Sampling - Part - 02

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    Why de we care about Markov Chains ?

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    Motivation for Sampling

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    Computing the gradient of the log likelihood

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Week 5

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

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    Training RBMs Using Gibbs Sampling

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    Training RBMS Using Contrastive Divergence

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Week 6

NOC:Deep Learning - Part 2
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    Revisiting Autoencoders

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    Variational Autoencoders: The Neural Network Perspective

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    Variational Autoencoders: The Graphical model perspective

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Week 7

NOC:Deep Learning - Part 2
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    Neural Autoregressive Density Estimator

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    Masked Autoencoder Density Estimator (MADE)

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Week 8

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

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    Generative Adversarial Networks - Architecture

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    Generative Adversarial Networks - The Math Behind it

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    Generative Adversarial Networks - Some Cool Stuff and Applications

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    Bringing it all together (the deep generative summary)

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