Genetic Algorithms & Neural Networks: A Practical Approach
Published 4/2024
Duration: 6h4m | .MP4 1280x720, 30 fps(r) | AAC, 44100 Hz, 2ch | 1.75 GB
Genre: eLearning | Language: English [/align]
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.
Requirements
Basic programming - Java, but experience in any programming language is beneficial for this course
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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