|
via Udemy |
Go to Course: https://www.udemy.com/course/genetic-algorithm-in-cpp/
Review and Recommendation for the Coursera Course: "C++ Machine Learning Algorithms Inspired by Nature" Overview: The course "C++ Machine Learning Algorithms Inspired by Nature" is a compelling choice for students and developers eager to deepen their understanding of optimization algorithms through hands-on coding in C++. Unlike many courses that rely heavily on libraries, this course emphasizes building algorithms from scratch, offering a true mastery of the underlying principles. Content & Topics: This course covers some of the most renowned AI algorithms inspired by natural processes, including Genetic Algorithms (GA), Simulated Annealing (SA), Differential Evolution, and Ant Colony Optimization (ACO). Each algorithm is explored both theoretically and practically, with real problem-solving applications such as the Traveling Salesperson Problem and the Knapsack Problem. The course begins with foundational concepts—what optimization algorithms are and when to use them—then progresses into detailed implementations. Students will learn how to code crossover and mutation in genetic algorithms, implement simulated annealing for function optimization, explore differential evolution strategies, and understand ant colony inspiration for solving discrete problems. Prerequisites & Recommendations: Prerequisites include a basic understanding of C++, familiarity with algorithms, and some mathematical background. The instructor recommends engaging actively by coding along with the videos, which is essential for internalizing these complex concepts. Strengths: - Hands-on approach: Coding algorithms from scratch enhances understanding. - Covers both continuous and discrete problems. - Exposure to lesser-known algorithms like Differential Evolution and Ant Colony Optimization adds value. - Suitable for intermediate to advanced learners with some programming and mathematical background. Potential Drawbacks: - No syllabus is provided, so learners might need to pace themselves. - Assumes familiarity with C++, which could be challenging for absolute beginners. Would I Recommend This Course? Yes, especially for those who have a foundational knowledge of C++ and algorithms and are looking to expand their expertise into AI-inspired optimization techniques. Its practical coding approach makes it highly effective for mastering these algorithms. Final Verdict: If you're interested in AI, optimization, and C++, and want to learn through building actual algorithms without relying on libraries, this course is an excellent investment. It combines theory with practical implementation, making complex concepts accessible and engaging. Enroll now to enhance your skills and portfolio of machine learning techniques with natural inspirations!
This online course is for students and software developers who want to level up their skills by learning interesting optimization algorithms in C++.You will learn some of the most famous AI algorithms by writing it in C++ from scratch, so we will not use any libraries. We will start with the Genetic Algorithm (GA), continue with Simulated Annealing (SA) and then touch on a less known one: Differential Evolution. Finally, we will look at Ant Colony Optimization (ACO).The Genetic Algorithm is the most famous one in a class called metaheuristics or optimization algorithms. You will learn what optimization algorithms are, when to use them, and then you will solve two problems with the Genetic Algorithm(GA). The second most famous one is Simulated Annealing. However, nature gives us fascinating sources of inspiration, such as the behaviour of ants, so that Ant Colony Optimization is an interesting algorithm as well.We will solve continuous problems(find the maximum/minimum of a continuous function) and discrete problems, such as the Travelling Salesperson Problem (TSP), where you have to find the shortest path in a network of cities, or the Knapsack Problem.Prerequisites:understand basic C++any C++ IDE (I am using Visual Studio)understanding of algorithmsunderstand mathematicsI recommend that you do the examples yourself, instead of passively watching the videos.Here's a brief outline of what you will learn:What optimization algorithms areGenetic Algorithm theory:General structureHow crossover is doneHow mutation is doneGenetic Algorithm on a continuous problem:Challenges particular to continuous problems: decoding the bits ("chromosomes") into a float valueCrossover: tournament selection and single point crossoverMutationGenetic Algorithm on the TSP (Travelling Salesperson Problem):Creating a fitness function for the TSPChallenge particular to this problem: how to do crossover?MutationSimulated Annealing:Basic TheoryOptimizing Himmelblau's functionThe knapsack problemDifferential Evolution:Theory and different strategiesCode example on one strategy, the standard one (DE/rand/1/bin)Ant Colony Optimization:Theory and InspirationExample on the Travelling Salesperson ProblemSign up now and let's get started!