Deep Learning III

Trainig and Internship

Introduction to Deep Learning with Python

What you'll learn
  • All the concepts GAN
  • Reinforcement learning
  • Encoders & Decoders
  • Certifications – Certificate will be provide after complication of the course.
  • Available –Online / Offline Learning (No recorded lecture / live session of each class).
  • 60 hrs Course..
  • Rich course content for master learning
  • Pay after six demo class.
  • 100% Hands on Course + 30 %Theory.
  • One MCQ and one case study is compulsory for completion of course..
  • Certificate will generated after successfully completion of the course.
  • Mathematics and Statistics.
  • Python
  • Machine learning
  • Deep learning


Probability Theory, Joint Distributions, Represent the joint distribution more compactly, graphical representation of joint distribution, Different types of reasoning encoded in a Bayesian Network, Independencies encoded by a Bayesian Network .


Markov Networks: Motivation, Factors in Markov Network, Local Independencies in a Markov Network, Computing the gradient of the log likelihood, Sampling, Concept of a latent variable, Restricted Boltzmann Machines, RBMs as Stochastic Neural Networks, Unsupervised Learning with RBMs, Markov Chains, Setting up a Markov Chain for RBMs, Training RBMs Using Gibbs Sampling, Training RBMS Using Contrastive Divergence.


Autoencoders, VariationalAutoencoders, Graphical model perspective, Neural Autoregressive Density Estimator (NADE), Masked Autoencoder Density Estimator (MADE), Generative Adversarial Networks.


Introduction to Reinforcement Learning, Bandit algorithms, Policy gradient methods, Dynamic programming and Monte carlo method, Model free prediction and control, DNQ Learning, Actor critics, Case study in RL.