Overview What You Will Learn Curriculum Instructor

Course Overview

In your regular tasks, you might come across cases wherein you will have to process unlabeled data and find hidden patterns from the given dataset. You would require unsupervised learning techniques to solve such types of cases in machine learning. Unsupervised learning is a technique that does not require supervision of the model and enables models to work on their own and find hidden, undetected patterns.

This course dives deeper into unsupervised deep learning algorithms. This course begins with an introduction of principal component analysis and its uses. Further, you will learn about the uses and limitations of t-SNE. You will understand what are autoencoders and how to write them in Theano and TensorFlow. This course delves deeper into restricted Boltzmann machines and their applications. You will learn to use it. Furthermore, you will learn how to use natural language processing in your daily tasks in a hands-on manner.

By the end of this course, you will be able to develop, deploy, and secure your deep learning models and run deep learning experiments effectively.

What You Will Learn

  • Master the fundamentals of unsupervised deep learning
  • Deep dive into various models and algorithms used in unsupervised deep learning and their applications
  • Get equipped with principal component analysis and its use in daily tasks
  • Develop advanced conceptual understanding of NLP and its use for data processing
  • Learn how unsupervised learning can be used in complex cases with unlabeled data

Program Curriculum

  • Introduction, Structure, and Outline of the Course
  • Details on Codes and Repository
  • TensorFlow or Theano
  • Real-world Applications of Unsupervised Learning
  • $7 Million Cybersecurity Scholarship by EC-Council

  • Introduction
  • PCA Numerical Analysis - Part 1
  • PCA Numerical Analysis - Part 2
  • PCA Only Rotates
  • MNIST Visualization
  • PCA Implementation
  • PCA for NLP
  • PCA Objective Function
  • Application of PCA – Naive Bayes
  • Singular Value Decomposition
  • Chapter 2 Quiz

  • t-SNE Theory
  • t-SNE Visualization
  • t-SNE on the Donut
  • t-SNE on the XOR
  • t-SNE on the MNIST
  • Chapter 3 Quiz

  • Introduction
  • Denoising Autoencoders
  • Stacked Autoencoders
  • Writing Autoencoder Class (Theano)
  • Writing the Deep Neural Network Class in Code (Theano)
  • Autoencoder in Code (TensorFlow)
  • Chapter 4 Quiz

  • Introduction
  • Restricted Boltzmann Machines – Working
  • Restricted Boltzmann Machines – Training
  • Restricted Boltzmann Machines - Code (Theano)
  • Restricted Boltzmann Machines – Code (TensorFlow)
  • Chapter 5 Quiz

  • Overview
  • Latent Semantic Analysis - Intuition
  • Latent Semantic Analysis - Maths
  • Latent Semantic Analysis - Code
  • Chapter 6 Quiz

  • Overview
  • Recommender System – Autoencoder and RBM
  • Chapter 7 Quiz
Load more modules

Instructor

Aashish Dixit

Aashish has spent a lot of his life studying and teaching. He holds a master’s and bachelor’s degree in Engineering. He has also spent a lot of his life working as a senior Network Data engineer at GE & HP in Gurgaon (& San Jose), then as a Manager Data N/W, first with JP Morgan Chase, Ohio and then on his own, and finally at Flipkart and Cisco in Bangalore. In all these roles, he has written a lot of code and built a lot of models. He has spent around two decades teaching high-quality, in-depth courses on advanced Mathematics, Data Science & Technology. I have helped hundreds of learners become champions in subjects and enabled them to change their lives, with his students working at companies such as Amazon, Google, Cisco, and Facebook.

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