Mô tả

Do you know how new branches like machine learning and data science come into existence?

It's because now there's tremendous demand for artificial neural networks across a range of industries.


The power of artificial intelligence is now being used by us without our knowledge or surprise. But do you know that the first neuron was introduced just in the year 1943.


McCullock and Pitts presented the first formal model of the neuron as an elementary commuting technique in this year.

Since then, a rising number of potential use cases for ANN and algorithms have been published.


The use of ANN has increased dramatically over the past ten years. Although learning algorithms and data management are essential components of ANN, research and development on them have advanced at such a rate that we now need to establish independent branches. These fields are now referred to as machine learning and data science, respectively.


This course is designed for those who want to learn the Artificial Neural Network from scratch.


It doesn’t matter which software you are familiar with or which branch you belong to.

You will gain a foundational understanding of what an artificial neural network is and how it functions after taking this course.


Once you've mastered the fundamentals, this course shows you how to create ANN code using MATLAB simulation and code.

Once it is finished, this course will assist you in developing an ANN model quickly using MATLAB's tools and apps.

So this course would be really beneficial if you want to start with artificial neural networks.


This is a first step toward ANN, and after completing this course, you will be able to go far with Artificial Neural Network and its application.

Enroll in the course and start learning Fundamentals of Artificial Neural Network with MATLAB.

Bạn sẽ học được gì

Applications of ANN

Working of Artificial Neuron

Interconnection of artificial neurons

Different Type of Networks

How to train ANN (Learning Algorithms)

How to design ANN for given problem

How to develop ANN with MATLAB Programing

How to develop ANN with MATLAB Simulations

How to use ANN along with other MATLAB simulations

How to develop ANN with MATLAB MAchine Learning Apps

Yêu cầu

  • To understand fundamentals of Artificial Neural Network, you don’t require any prerequisite. Even school students who know simple equations of line, can also understand the basics of Artificial Neural Networks.
  • However primary knowledge of MATLAB may be required to quickly grasp the content of former chapters.

Nội dung khoá học

12 sections

Introduction

5 lectures
1.1 Course Introduction
05:23
1.2 Introduction to ANN
07:39
1.3 Classification Problem
08:58
1.4 Regression Problem
04:58
1.5 Pattern Recognition Problem
04:15

Ch 2: Mathematical Representation and concept

3 lectures
2.1 Mathematical Representation of Classification and Regression Problem
12:54
2.2 Line Equation to Neuron Structure
13:08
2.3 First Model of Artificial Neuron
06:18

Ch 3: Understand Artificial Neuron Model with Practice

4 lectures
3.1 Further Simplified Neuron Model for Practice
03:08
3.2 Find Decision Boundary for Given Neuron
09:10
3.3 Neuron vs Equation of line
01:38
3.4 Find Neuron parameters from decision boundary
04:24

Ch 4: Implementation of Digital Logic with Neurons

5 lectures
4.1 Review and Intro
02:40
4.2 Implement NOT Gate with Neuron
05:33
4.3 Implement AND Gate with Neuron
04:45
4.4 Implement OR Gate with Neuron
03:38
4.5 Implement XOR Gate with Neuron
02:25

Ch 5: Interconnection of Neurons

6 lectures
5.1 Interconnection of Neurons
09:27
5.2 Feed forward Network
07:56
5.3 Tutorial 5.1
09:56
5.4 Tutorial 5.2
08:23
5.5 Tutorial 5.3
10:04
5.6 Summary
02:17

Ch 6: Learning Algorithm

4 lectures
6.1 Learning algorithm
04:18
6.2 Perceptron learning rule
07:04
6.3 Perceptron Learning rule with an Example
18:51
6.4 Analysis and Summary of Perceptron Learning
13:03

Ch 7: Basics of ANN design with MATLAB

6 lectures
7.1 General Requirement of designing ANN
06:18
7.2 Perceptron Practice 2
13:08
7.3: Develop Trained ANN with MATLAB Programing
11:27
7.4: Develop Trained ANN with MALTAB Simulink
07:27
7.5: Learning Algorithm with MATLAB Programing
21:45
7.6: Classification Problem with MATLAB Tool Box
08:37

Ch 8: ANN for Regression Problem (Curve fitting)

8 lectures
8.1: Linear Regression & Non-linear Regression
09:11
8.2: Delta Learning Rule
07:48
8.3: Finding Derivative term for different activation Functions
08:45
8.4: Trained Continuous Perceptron with MATLAB Programing
05:31
8.5 Writing MATLAB Code for Delta Learning Rule
09:24
8.6 Develop Simulation Model of Continuous Peceptron
03:03
8.7 Design Continuous Perceptron with Manual Weight Calculation
07:17
8.8: Train Continuous Perceptron with MATLAB
08:13

Ch 9: Two Layer Feed Forward Network MATLAB NN Fitting App

4 lectures
9.1: Introduction to Two Layer Feed Forward Network
03:08
9.2: Curve Fitting Practice 1 with MATLAB NN Toolbox
12:47
9.3: How to use Generated Simulink Model
03:24
9.4: Curve Fitting Practice 2 with MATLAB NN Toolbox
03:56

Ch 10: Case Study: Design ANN for Car Price Prediction with MATLAB

4 lectures
10.1: Decide NN Type and Learning Algorithm
04:12
10.2: Analyse Data
03:46
10.3: Develop ANN for Car Price Prediction with NN Fitting App
05:24
10.4: Develop Simulation Model for Car Price Prediction
04:15

Ch 11: Case Study: ANN-Based Sensorless Switched Reluctance Motor Drive

4 lectures
12.1 Understand the Problem of Rotor Position Estimation of SRM
05:56
12.2 Design an ANN for Rotor Position Estimation of SRM
03:57
12.3 Performance Analysis of ANN Trained for Rotor Position Estimation
04:16
12.4 Simulation of ANN Based Sensorless SRM Drive
04:47

Ch 12: Comprehensive Summary

3 lectures
12.1 Course Summary
06:13
12.2 What Next?
07:53
Bonus Lecture
01:36

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