Genetic Algorithms & Neural Networks: Java, AI

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Catalin Baba

6:08:02

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1. Introduction

1. Introduction.mp4

02:51

2. Neuro-Evolution.mp4

05:10

2. Foundations of Genetic Algorithms

1. Introduction to Genetic Algorithms.mp4

09:44

2. Core Principles of Genetic Algorithms.mp4

07:40

3. Genetic Algorithm Architecture.mp4

11:55

4. Parent Selection in Genetic Algorithms.mp4

24:33

5. Genetic Algorithms The Crossover Process.mp4

17:24

6. The Mutation Process in Genetic Evolution.mp4

10:13

7. Survivor Selection in Genetic Algorithms.mp4

06:06

8. Pros and Cons of Genetic Algorithms.mp4

05:02

3. Hands-On with Genetic Algorithms

1. Setting Up Your Genetic Algorithm Environment.mp4

04:16

2.1 lesson1.zip

2. Hello World of Genetic Algorithms Binary String Optimization 1.mp4

29:48

3. Hello World of Genetic Algorithms Binary String Optimization 2.mp4

18:23

4. Genetic Algorithms The Role of Population Size.mp4

03:32

5.1 lesson2.zip

5. Applying Genetic Algorithms to the Traveling Salesman Problem 1.mp4

25:59

6. Applying Genetic Algorithms to the Traveling Salesman Problem 2.mp4

14:23

7.1 lesson3.zip

7. Solving Sudoku with Genetic Algorithms 1.mp4

33:48

8.1 customcrossover.zip

8.2 custommutator.zip

8.3 parentcustom.zip

8. Solving Sudoku with Genetic Algorithms 2 (Customize GA Operators).mp4

14:58

9.1 lesson4.zip

9. Optimizing Functions Using Genetic Algorithms.mp4

13:22

4. Fundamentals of Neural Networks

1. Fundamentals of Neural Networks.mp4

20:46

5. Hands-On with Genetic Algorithms and Neural Networks

1.1 lesson5.zip

1.2 myfirstnet.zip

1. Digit Recognition with Neural Networks Building and Training.mp4

19:01

2.1 lesson6.zip

2. Digit Recognition Training Neural Networks with Genetic Algorithms.mp4

15:35

6. Neuro-evolution in Action Evolving the Snake Game

1. Snake Game - Overview.mp4

01:48

2. Creating a Neural Network for the Snake Game.mp4

06:35

3.1 playsnake.zip

3.2 snakefitness.zip

3.3 snakega.zip

3.4 testsnake.zip

3.5 world.zip

3.6 worldgui.zip

3. Foundational Helper Classes Simplifying Your Project Workflow.mp4

05:29

4.1 snakega.zip

4. Setting Up the Genetic Algorithm for the Snake Game.mp4

08:11

5.1 snakefitness.zip

5. Designing the Fitness Function for the Snake Game.mp4

16:21

6.1 playsnake.zip

6.2 snakefitness.zip

6.3 snakega.zip

6.4 testsnake.zip

6.5 world.zip

6.6 worldgui.zip

6. Tracking Snake Evolution and Performance.mp4

15:09

Description

Genetic Algorithms, Neural Networks, AI, Neuro-Evolution, Java

What You'll Learn?

Genetic Algorithms: Master optimization with evolutionary computation
Genetic Algorithms: Improve and enhance the genetic algorithm
Artificial Intelligence: Explore cutting-edge AI techniques for solving complex problems.
Neuro-Evolution: Dive into evolving neural networks for adaptive solutions.
Neural Networks: Utilize machine learning for advanced pattern recognition.

Who is this for?

Students

AI enthusiasts

What You Need to Know?

Basic programming - Java, but experience in any programming language is beneficial for this course

More details

Description
Course Overview
Explore the cutting-edge of artificial intelligence with our detailed course on Genetic Algorithms and Neural Networks. This course is structured to take you from a theoretical understanding of complex algorithms to direct, hands-on application through a series of engaging activities and real-world problems. Perfect for those looking to deepen their AI expertise, the course covers everything from basic structures and functions to advanced applications in games and pattern recognition.

Learning Objectives
By the end of this course, students will:
Understand the principles and components of Genetic Algorithms, including selection, crossover, and mutation processes.
Gain practical experience with Genetic Algorithms by solving problems like the Traveling Salesman and function optimization.
Learn the basics of Neural Networks and apply them to real-world tasks such as digit recognition.
Develop an understanding of neuro-evolution techniques by creating a self-learning "Snake Game".
Critically analyze the advantages and limitations of these AI techniques and their applications.
Target Audience
This course is designed for:
Students and professionals interested in advanced AI technologies.
Data scientists and engineers looking to add sophisticated algorithmic methods to their toolkits.
Course Modules
Theory
Genetic Algorithm Overview: Introduction and history.
Fundamentals: Basic structure, parent selection, crossover, mutation, and survivor selection.
Evaluation: Advantages and disadvantages of Genetic Algorithms.
Practical Activities with Genetic Algorithms
"Hello World" Introduction: Basic implementation.
Traveling Salesman Problem: Optimization of a classic computational problem.
Function Optimization: Maximizing or minimizing function values.
Sudoku Solver: Applying Genetic Algorithms to solve Sudoku puzzles efficiently.
Neural Networks Overview
Basics of Neural Network Architecture: Understanding layers, neurons, and activation functions.
Learning and Adaptation: How networks learn and evolve over time.
Practical Activities with Neural Networks
Digit Recognition: Using Neural Networks to recognize and interpret handwritten digits.
Advanced Application: Neuro-evolution in Games
Snake Game: Developing an AI that learns to play Snake using both Genetic Algorithms and Neural Networks.
Dive into the world of Genetic Algorithms and Neural Networks with our structured, practical approach that balances theory with extensive hands-on experience. Enroll today to start transforming theoretical knowledge into impactful solutions and innovations in the field of artificial intelligence.
Who this course is for:
Students
AI enthusiasts

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Hi! I have a degree in Electrical Engineering from University Politehnica of Bucharest, Romania. I have been working as Automation Software Engineer for more than 6 years. During this time I have created programs to automate testing for Avaya L3 switches. I have also manually tested different Nortel/Avaya OS`s and I have worked with many network protocols like: STP, RSTP, MSTP, SNMP, RIP, OSPF, IPV4, & IPV6, security features for L2/L3, Avaya Network Virtualization. I have gained experience in network communication field and also in software development. I started to learn java 10 years ago and since then I have created many java applications like networks applications, GUI applications or programs used to automate my daily tasks. Some weeks ago I decided to gather my experience in networking and programing and create a Java network programming course. It was a new challenge for me, and after long hours of research, recording and editing I have successfully created a network programming course for students and also for programmers.

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