4.5 out of 5
445 reviews on Udemy

Artificial Intelligence II – Hands-On Neural Networks (Java)

Hopfield networks, neural networks, gradient descent and backpropagation algorithms explained step by step
Holczer Balazs
4,731 students enrolled
English [Auto] More
Basics of neural networks
Hopfield networks
Concrete implementation of neural networks
Optical character recognition

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

  • how to construct an autoassociative memory with neural networks

Section 3:

  • what is back-propagation

  • feedforward neural networks

  • optimizing the cost function

  • error calculation

  • backpropagation and gradient descent

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)

  • smile-detector application from scratch

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!

You can view and review the lecture materials indefinitely, like an on-demand channel.
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5 hours on-demand video
1 article
Full lifetime access
Access on mobile and TV
Certificate of Completion
Artificial Intelligence II – Hands-On Neural Networks (Java)
$218.98 $169


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