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Artificial Intelligence II – Neural Networks In Java

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Hopfield networks, neural networks, backpropagation, optical character recognition, feedforward networks

What you’ll learn

  • Basics of neural networks
  • Hopfield networks
  • Concrete implementation of neural networks
  • Backpropagation
  • Optical character recognition
Requirements
  • Basic Java
Description
This course is about artificial neural networks. Artificial intelligence and machine learning are getting more and more popular nowadays. In the beginning, other techniques such as Support Vector Machines outperformed neural networks, but in the 21th century neural networks again gain popularity. In spite of the slow training procedure, neural networks can be very powerful. Applications ranges from regression problems to optical character recognition and face detection.
Section 1:

  • what are neural networks
  • modeling the human brain
  • the big picture
Section 2:
  • Hopfield neural networks
Section 3:

  • what is back-propagation
  • feedforward neural networks
  • optimizing the cost function
  • error calculation
  • backpropagation and resilient propagation
Section 4:
  • the single perceptron model
  • solving linear classification problems
  • logical operators (AND and XOR operation)
Section 5:
  • applications of neural networks
  • clustering
  • classification (Iris-dataset)
  • optical character recognition (OCR)
In the first part of the course you will learn about the theoretical background of neural networks, later you will learn how to implement them.

If you are keen on learning methods, let’s get started!
Who this course is for:

  • This course is recommended for students who are interested in artificial intelligence focusing on neural networks
Created by Holczer Balazs
Last updated 4/2019
English
English [Auto-generated]
Size: 818.25 MB
Author
EVILEAKS
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