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Artificial Neural Network

This course covers Artificial Neural Networks and Deep Learning techniques. These techniques are fast becoming popular and they are finding application in many fields. The course takes a practical and “hands-on”approach to teaching Machine Learning. Concepts will be introduced through practical examples and participants will be given exercises to be completed within and outside of contact hours. Use of the popular deep learning tool – Tensorflow.Concepts of Artificial Neural Networks and Deep Learning Networks. Participants will understand these topics through practical tools like Tensorflow.

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Training Highlights

  • The course takes a practical and “hands-on”approach to teaching Machine Learning.
  • Concepts will be introduced through practical examples and participants will be given exercises to be completed within and outside of contact hours. Use of the popular deep learning tool – Tensorflow multi-node Spark cluster

What One Will Learn

  • Concepts of Artificial Neural Networks and Deep Learning Networks.
  • Participants will understand these topics through practical tools like Tensorflow.

Prerequisite

  • Proficiency over Python programming language is a must.
  • Good understanding of statistical machine learning techniques is a definite pre-requisite.

Curriculum

Module 1
  • Course Overview and Pedagogy
Module 2

Why ANN and Deep Learning

  • Concepts

  • Examples and Exercises

Module 3

  • Introduction to Deep Learning

    • Machine v/s Deep Learning

    • Architectures

    • Deep Learning Tools – Overview

  • Deep Learning Tools – Overview

    • Installation & setup

    • Non-GPU and GPU versions

Module 4

Tensorflow

  • Overview of Interfaces

Module 5

Tensorflow – Part 2

  • Creating Tensorflow Models

  • Exercises

Module 6

Tensorflow – Part 3

  • Convolutional Neural Nets

  • Exercise: Digit Recognition

  • Exercise: Image Recognition

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