Deep Learning

What is this course about?

Deep Learning is a sub-field of Machine learning that concentrate only of several Neural Network architecture for better and optimised learning in Real-Time. With advent of Deep Neural Network, Machines had started to gain immense amount of intelligence that even made them to defeat humans in several fields.

This Course will brief through several Neural Network Architecture.

What are the course objectives?

Unlike other Machine Learning concepts, Deep learning needs very narrow and optimized thinking. This course will help you in tuning your brain to get to understand how it works on its own represented in mathematical model.

Who should do this course?

This course can be benefited by people having some pretty good idea on conventional Machine learning systems and its application with some decent programming experience. To be precise, student, developers and other research professional can unlock much more on their work skill using this course.

Job Opportunity for information?

Several software companies and Research institutions are looking for dynamic minds having immense experience in Deep learning in both theoretical and practical way with pretty good open source contribution and research papers.

Course Duration Information

Program Schedule

• Mode: Face to face training in batches with a maximum batch size of 5
• Total number of hours: 60 hours; Weekday Classes; 2 hours/ Day; 5days/Week

Part 1: Introduction

• Machine Learning at a Glance.
• Narrowing to Deep Learning.
• History of Machine Learning.
• Python overview.

Part 2: Mathematical Basics

• Linear Algebra
• Probability and Information Theory
• Numerical Analysis and Computation.

Part 3: Programming Foundation

• Python for Deep Learning.
• GIT basics
• Intro to Deep Learning Frameworks.
• Working with Data.
• Exercise 1

Part 4: Getting Started

• Feed Forward Network.
• Multilayer Perceptron.
• Exercise 2
• Convolutional Neural Network.
• Classifying MNIST digit Using Logistic Regression.
• Exercise 3

Part 5: Unconventional Architectures:

• Autoencoder
• Denoising Autoencoder.
• Boltzmann Machine.
• Restricted Boltzmann Machine.
• Deep Belief Networks.
• Exercise 4

Part 6: Sequence Modelling

• Recurrent Neural Network (RNN)
• Bidirectional RNNs
• Long Short-Term Memory.
• Gated Recurrent Units

Part 7: Conclusion:

• Deep Learning – Research perspective.
• How to proceed further.

At end of our course, you will be assigned to work a real-time project. Once you completed assigned project with expected results we (Experts Team from Relearn Pro) will verify and issue information Certificate. If you are not able to deliver expected results in project we will support you by clarifying doubts and help you to re-attempt the project.