Artificial Intelligence II - Neural Networks in Java
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Artificial Intelligence II - Neural Networks in Java

Hopfield networks, neural networks, backpropagation, optical character recognition, feedworfard networks
Bestselling
4.4 (64 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
985 students enrolled
Created by Holczer Balazs
Last updated 5/2017
English
Current price: $10 Original price: $35 Discount: 71% off
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Includes:
  • 5 hours on-demand video
  • 3 Articles
  • 3 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Basics of neural networks
  • Hopfield networks
  • Concrete implementation of neural networks
  • Backpropagation
  • Optical character recognition
View Curriculum
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. 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 is the target audience?
  • This course is recommended for students who are interested in artificial intelligence focusing on neural networks
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Curriculum For This Course
61 Lectures
04:50:28
+
Introduction
1 Lecture 03:08
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Neural Networks Introduction
8 Lectures 43:27

Modeling human brain
07:16

Learning paradigms
03:00

Artificial neurons - the model
06:57

Artificial neurons - activations functions
06:16

Artificial neurons - an example
05:00

Neural networks - the big picture
04:33

Applications of neural networks
02:12
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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
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Neural Networks With Backpropagation Theory
14 Lectures 01:20:38
Feedforward neural networks
08:10

Optimization - cost function
10:40

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

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
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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
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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
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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
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Clustering
2 Lectures 06:55
Clustering with neural networks I
02:08

Clustering with neural networks II
04:47
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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
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Optical Character Recognition (OCR)
5 Lectures 18:19
Optical character recognition theory
03:33

Installing paint.net
02:35

Transform an image into numerical data
04:18

Creating the datasets
02:00

OCR with neural network
05:53
1 More Section
About the Instructor
Holczer Balazs
4.5 Average rating
2,902 Reviews
29,250 Students
21 Courses
Software Engineer

Hi!

My name is Balazs Holczer. I am from Budapest, Hungary. I am qualified as a physicist and later on I decided to get a master degree in applied mathematics. At the moment I am working as a simulation engineer at a multinational company. I have been interested in algorithms and data structures and its implementations especially in Java since university. Later on I got acquainted with machine learning techniques, artificial intelligence, numerical methods and recipes such as solving differential equations, linear algebra, interpolation and extrapolation. These things may prove to be very very important in several fields: software engineering, research and development or investment banking. I have a special addiction to quantitative models such as the Black-Scholes model, or the Merton-model. Quantitative analysts use these algorithms and numerical techniques on daily basis so in my opinion these topics are definitely worth learning.

Take a look at my website and join my email list if you are interested in these topics!