Artificial Intelligence II - Neural Networks in Java
4.2 (275 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
3,344 students enrolled

Artificial Intelligence II - Neural Networks in Java

Hopfield networks, neural networks, backpropagation, optical character recognition, feedforward networks
4.2 (275 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
3,344 students enrolled
Created by Holczer Balazs
Last updated 4/2019
English
English [Auto-generated], Indonesian [Auto-generated], 3 more
  • Polish [Auto-generated]
  • Romanian [Auto-generated]
  • Thai [Auto-generated]
Current price: $11.99 Original price: $199.99 Discount: 94% off
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This course includes
  • 5 hours on-demand video
  • 5 articles
  • 1 downloadable resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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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
Course content
Expand all 64 lectures 04:54:59
+ Neural Networks Introduction
9 lectures 43:30
Modeling human brain
07:16
Learning paradigms
03:00
Artificial neurons - the model
06:57
Artificial neurons - activations functions
06:16
ARTICLE: activation functions
00:03
Artificial neurons - an example
05:00
Neural networks - the big picture
04:33
Applications of neural networks
02:12
+ Hopfield Neural Network
9 lectures 47:56
Hopfield neural network training and learning
04:59
Hopfield neural network problems
03:16
Hopfield neural network example
05:49
Hopfield network implementation I - utils
04:07
Hopfield network implementation II - matrix operations
08:45
Hopfield network implementation III - network
07:39
Hopfield network implementation IV - running the application
04:08
+ Neural Networks With Backpropagation Theory
16 lectures 01:20:46
Feedforward neural networks
08:10
Optimization - cost function
10:40
ARTICLE: optimization algorithms
00:05
Simplified feedforward network
08:07
Feedforward neural network topology
06:04
The learning algorithm
05:17
Error calculation
06:06
Gradient calculation I - output layer
08:21
Gradient calculation II - hidden layer
03:49
Backpropagation
05:18
Backpropagation II
01:59
ARTICLE: derivation of backpropagation
00:03
Resilient propagation
04:20
Applications of neural networks I - character recognition
04:06
Applications of neural networks II - stock market forecast
04:10
Deep learning
04:11
+ Single Perceptron Model
6 lectures 23:24
Perceptron model training
02:00
Perceptron model implementation I
05:10
Perceptron model implementation II
05:42
Perceptron model implementation III
06:03
Trying to solve XOR problem
01:29
Conclusion: linearity and hidden layers
03:00
+ Backpropagation Implementation
6 lectures 38:24
Structure of the feedforward network
05:38
Backpropagation implementation I - activation function
04:45
Backpropagation implementation II - NeuralNetwork
08:25
Backpropagation implementation III - Layer
05:32
Backpropagation implementation IV - run
07:03
Backpropagation implementation V - train
07:01
+ Logical Operators
4 lectures 15:53
Logical operators introduction
02:06
Running the neural network: AND
08:00
Running the neural network: OR
03:16
Running the neural network: XOR
02:31
+ Clustering
2 lectures 06:55
Clustering with neural networks I
02:08
Clustering with neural networks II
04:47
+ Classification - Iris Dataset
3 lectures 12:20
About the Iris dataset
02:47
Constructing the neural network
02:39
Testing the neural network
06:54