IKH

Introduction to Neural Network

Welcome to this course on Neural Networks.

This course is divided into two parts. In the first part- Introduction to Neural Networks: Part 1, you will learn the architecture of a Neural Network and the working principles of artificial neural networks, including information flow in feedforward networks.

In the second part- Introduction to Neural Networks: Part 2, you will learn to implement neural networks using Keras (a high-level, user-friendly interface) with TensorFlow (a low-level workhorse library) as the back end. You will be able to build a neural network, modify its architecture, optimise and train it. 

In this Module

In this module, you will learn about – what are arguably the most sophisticated and cutting-edge models in machine learning – Artificial Neural Networks (or ANNs). Inspired by the structure of the human brain, neural networks have established a reputation for successfully learning complex tasks such as object recognition in images, automatic speech recognition (ASR), machine translation, image captioning, and video classification.

In this session

To begin with, you will get an intuitive idea about neural networks. The following topics will be covered in this session:

  • Structure of ANNs, inspired by the human brain
  • Perceptron: A simple idea as the basis for larger neural networks
  • Workings of artificial neurons
  • Structure and topology of neural networks
  • Hyperparameters and simplifying assumptions

Prerequisites

As the main prerequisites for this session, you must have a basic understanding of the concepts of vectors, matrix multiplication, derivatives, and partial derivatives and must have completed the previous courses on statistics and ML.

Guidelines for In-Module Questions

The in-video and in-content questions for this module are not graded. Note that graded questions are given in a separate segment titled ‘Graded Questions’ at the end of this session. The graded questions in this session will adhere to the following guidelines:

First Attempt MarksSecond Attempt Marks
Questions With 2 Attempts105
Questions With 1 Attempt100

People you will hear from in this session:

Subject Matter Expert:


G.Srinivasaraghavan

Professor, IIIT-Bangalore
G. Srinivasaraghavan, PhD is a Partner at Performance Engineering Associates. He has a PhD in Computer Science from the Indian Institute of Technology Kanpur and has over 18 years of industry experience. At Infosys Technologies, India’s premier IT firm, he was responsible for the delivery of large, performance-critical IT systems for Fortune 500 clients in the telecom, BFSI, and logistics spaces. He has over a dozen published papers in several reputed international fora, including journal of Algorithms, International Journal on Computational Geometry and Applications, and Foundations of Software Technolgy and Theoretical Computer Science. In his previous position, he was Chief Technology Officer at Aztecsoft Ltd(now a part of Mindtree Ltd), where he brought about a radical, product-quality-focussed shift in the firm’s approach to quality assessment.​


Gunnvant Singh Saini
Gunnvant is experienced analytics professional with a demonstrated history of working in EdTech. He has designed and helped launch machine learning, data science, and deep learning training programs for learners with varied skillsets and different career expectations. He has also been involved in tech advocacy. 

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