samcx.com
4.5 out of 5
4.5
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
Instructor:
Holczer Balazs
4,731 students enrolled
English [Auto] More
Basics of neural networks
Hopfield networks
Concrete implementation of neural networks
Backpropagation
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!

Introduction

1
Introduction

Artificial Intelligence Basics

1
Why to learn artificial intelligence and machine learning?
2
Types of artificial intelligence learning methods

Hopfield Neural Network Theory

1
Hopfield neural network introduction
2
Hopfield network - weights
3
Hopfield neural network - Hebbian learning
4
Hopfield neural network - energy
5
Measuring the energy of the network
6
Hopfield neural network example
7
Hopfield networks quiz

Hopfield Neural Network Implementation

1
Hopfield network implementation - utils
2
Hopfield network implementation - matrix operations
3
Hopfield network implementation - network
4
Hopfield network implementation - running the algorithm

Neural Networks With Backpropagation Theory

1
Artificial neural networks - inspiration
2
Artificial neural networks - layers
3
Artificial neural networks - the model
4
Why to use activation functions?
5
Neural networks - the big picture
6
Using bias nodes in the neural network
7
How to measure the error of the network?
8
Optimization with gradient descent
9
Gradient descent with backpropagation
10
Backpropagation explained
11
Applications of neural networks I - character recognition
12
Applications of neural networks II - stock market forecast
13
Deep learning
14
Types of neural networks
15
Neural networks quiz

Single Perceptron Model

1
Perceptron model training
2
Perceptron model implementation I
3
Perceptron model implementation II
4
Perceptron model implementation III
5
Trying to solve XOR problem
6
Conclusion: linearity and hidden layers

Backpropagation Implementation

1
Structure of the feedforward network
2
Backpropagation implementation I - activation function
3
Backpropagation implementation II - NeuralNetwork
4
Backpropagation implementation III - Layer
5
Backpropagation implementation IV - run
6
Backpropagation implementation V - train

Logical Operators

1
Logical operators introduction
2
Running the neural network: AND
3
Running the neural network: OR
4
Running the neural network: XOR

Clustering

1
Clustering with neural networks I
2
Clustering with neural networks II

Classification - Iris Dataset

1
About the Iris dataset
2
Constructing the neural network
3
Testing the neural network
4
Calculating the accuracy of the model

Optical Character Recognition (OCR)

1
Optical character recognition theory
2
Installing paint.net
3
Transform an image into numerical data
4
Creating the datasets
5
OCR with neural network

Course Materials (DOWNLOADS)

1
Course materials
You can view and review the lecture materials indefinitely, like an on-demand channel.
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
4.5
4.5 out of 5
445 Ratings

Detailed Rating

Stars 5
203
Stars 4
168
Stars 3
56
Stars 2
11
Stars 1
7
Get on Udemy
30-Day Money-Back Guarantee

Includes

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)
Category:
Data Science
Teacher
Daniel W
Price:
$218.98 $169
Get Course

Community

For Professionals

  • List Item #1
  • List Item #2
  • List Item #3

For Businesses

  • List Item #1
  • List Item #2
  • List Item #3

We support Sales, Marketing, Account Management and CX professionals. Learn new skills. Share your expertise. Connect with experts. Get inspired.

Linkedin Twitter Facebook

Community

Partnership Opportunities

  • Sponsorships
  • Advertise with us
  • List your Business
  • List your Event
  • List your Course

Company Information

  • About Us
  • Newsroom
  • Careers
  • Our Services
  • Contact us
Contribute now
Layer 1
samcx.com
Logo
Register New Account
Compare items
  • Total (0)
Compare
0