Neural Network

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Do you remember when you attended your first math class? You were unaware of additions & subtraction before it was taught to you. But today you can do it at your fingertips. This was possible only due to a lot of practice! All the gratefulness to our highly complex brains with billions of interconnected nodes (called neurons) that we can keep learning stuff.

Well, the concept of Neural Network is based on the same principle as our brain, even though it is impossible to replicate our brain, we can at least create a simple miniature model of it which can learn to perform the single task efficiently and then use that replicated brain to perform daily tasks.

Just like our brain contains neurons and synapses connecting them, Neural Networks also contain neurons, and the connection between these is called weights. Just like our sensory system sends our brain signal, Neural Network also sends the signal back using something known as backpropagation. Just as we improve our mistakes by comparing our actions with the right actions, Neural Networks also compare their results with actual results using something known as the cost function. Just like our brains tell us to do a specific action in a particular situation by sending signals to our muscles, Neural Networks also send signals using the process known as feedforward propagation.

This is just a basic example of the structure of one type of neural network. Yes, there are other types of Neural Networks as well, and we are going to discuss them in this course.

We will first start with a brief introduction to the concept of Neural Networks and the mathematics behind them and then continue looking at the different applications of Neural Networks using MATLAB and its Neural Network Toolbox.

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Neural Network

Introduction

1 Lesson

Discussion on data input, feedforward neural networks, error calculation and backpropagation.

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Data Fitting Application

2 Lessons

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Learn how to use a GUI for data fitting neural network.

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Learn how to create a Data fitting neural network for a random function and check its accuracy using different graphs.

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Time Series Application

2 Lessons

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Learn how to use a GUI for Timeseries neural network

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Predict the future values of the number of patients diagnosed with chickenpox.

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Pattern Recognition

2 Lessons

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The most popular area in which Neural Networks produce the highest accuracy is pattern recognition which will be discussed in this lesson.

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We will see an example of creating a pattern recognition neural network for classifying input into 4 different classes.

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Clustering application

2 Lessons

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Learn how to make use of GUI of Neural Network Toolbox in MATLAB to create a Clustering Neural Network.

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Let us create a Clustering Neural Network for segregating different colours from an image.

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Autoencoders

1 Lesson

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We will create a sparse autoencoder which will reconstruct the input, and further accuracy will be calculated.

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Application of Neural Network

1 Lesson

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Let us create a stacked deep Neural Network which will perform the task of recognizing the numbers from 0 to 9.

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Control Systems

4 Lessons

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This lesson gives a brief discussion of how neural networks can be used in designing the control system.

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Discussion on designing Neural Network Predictive Controller.

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Step-wise explanation of designing a Narma L2 controller.

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Let us see a complete model and explanation of a model reference controller.

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Radial basis function

1 Lesson

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We will create a Radial Basis Function Neural Network for a random function and check its accuracy using graphs.

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Certification Quiz

1 Lesson

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Text lesson

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MATLAB Helper

MATLAB Helper ® is an organization providing programming expertise with assistance to students, professionals, researchers & and corporate.

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