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

This is a Duplicate Course for Singaporeans picking up new skillsets and competencies under the CITREP+ Scheme.
0.0 (0 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.
1 student enrolled
Created by DioPACT SG
Last updated 6/2017
English
Price: $90
30-Day Money-Back Guarantee
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

Welcome to the SGLearn Series targeted at Singapore-based learners picking up new skillsets and competencies. This course is eligible for the CITREP+ funding scheme if you are a Singaporean above 16 years old, terms and conditions apply. Enjoy the course. 

Do note that this course on Artificial Intelligence is by Balazs Holczer and is duplicated for Singaporeans to enjoy the training subsidy from the Singapore government.

_____________ 

Note from Balazs Holczer

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
63 Lectures
05:11:13
+
Introduction
1 Lecture 03:08
+
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
+
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
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
+
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
+
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
2 More Sections
About the Instructor
DioPACT SG
4.5 Average rating
1 Review
7 Students
9 Courses
SGLearn

Dioworks is an e-learning design company focused on using technology as enablers to make learning easy, engaging and effective. Premised on innovative designs, pedagogy and research, we provide quality learning experiences for learners globally. Dioworks offers bespoke solutions for organisations to integrate learning, training and assessment of work-based competencies via blended learning strategies. We are also the local partner to Udemy in Singapore. 

More specifically, we combine the strengths of Classroom-Facilitated Learning, Massive Open Online Courses (MOOCs) in partnership with UDEMY Inc, and our "Kinetic Coach" automated response training solution to achieve learning outcomes.