|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/optimization-with-metaheuristics/
课程评论:没有评论
课程名称:Python中的元启发式优化 课程概述:本课程将指导您了解优化的概念以及元启发式算法的定义。您将学习在优化复杂问题时为什么需要元启发式方法,因为有时确定性方法无法找到最佳解决方案,因此元启发式算法非常重要。本课程涵盖了元启发式算法及其四种广泛使用的技术:模拟退火、遗传算法、禁忌搜索和进化策略。通过本课程,您将学习这些技术的基本原理、应用场景以及如何用Python从零开始进行编码,且不需要依赖任何现成的库和包。同时,您还将学习如何使用惩罚法处理约束条件。 课程亮点: - 不需要Python编程基础,课程会从最基础的知识开始教起。 - 每一行代码都会进行详细解释,确保您能够理解并掌握算法的实现。 - 课程结合理论与实践,您将在实际项目中应用所学知识。 - 如对课程不满意,可申请30天内退款。 学员评价: 1. 学员普遍认为该课程是他们在Udemy上所学到的最佳课程之一,教师Dana不仅解释了编码问题,还缓解了学习复杂概念时的焦虑。 2. 该课程提供了大量实际问题的应用案例,学生能够跟随课程逐步实现Python代码,感到如同一对一教学。 3. 学员表示课程是了解元启发式算法的最佳入门,课程内容不仅包含理论,还涵盖了实际算法的编写,强调了代码的可读性。 如果您对优化和Python编程感兴趣,欢迎加入这个课程,开启您的学习之旅!
This course will guide you on what optimization is and what metaheuristics are. You will learn why we use metaheuristics in optimization problems as sometimes, when you have a complex problem you'd like to optimize, deterministic methods will not do; you will not be able to reach the best and optimal solution to your problem, therefore, metaheuristics should be used.This course covers information on metaheuristics and four widely used techniques which are:Simulated AnnealingGenetic AlgorithmTabu SearchEvolutionary StrategiesBy the end of this course, you will learn what Simulated Annealing, Genetic Algorithm, Tabu Search, and Evolutionary Strategies are, why they are used, how they work, and best of all, how to code them in Python! With no packages and no libraries, learn to code them from scratch!! You will also learn how to handle constraints using the penalty method.Here's the awesome part -> you do NOT need to know Python programming!This course will teach you how to optimize continuous and combinatorial problems using PythonWhere every single line of code is explained thoroughlyThe code is written in a simple manner that you will understand how things work and how to code the algorithms even with zero knowledge in PythonBasically, you can think of this as not only a course that teaches you 4 well known metaheuristics, but also Python programming!Please feel free to ask me any question! Don't like the course? Ask for a 30-day refund!!Real Testaments ->1) "I can say that this is the best course I've had on Udemy! Dana is a very good instructor. She not only explains the problems and the coding, but also reassures you and remove the fears you might have when learning complex concepts. For someone with a business background, this topic was close to a nightmare! I highly recommend this course for anyone interested in learning about Metaheuristics. Again, big THANK YOU Dana!:)" - Logistics Knowledge Bank, 5 star rating2) "I am half way through the course. What I learnt so far is far beyond what I expected. What I really liked is the applicability of the examples to real world problems. The most exciting feature in the course is the hands on, what you learn will be implemented in python and you can follow every single step. If you did not understand, the instructor is there to help. I even felt like it is a one to one course. Thanks a lot to the instructor." - Ali, 5 star rating3) "The best introduction to Metaheuristics bar none. Best value course on Udemy. I love that we cover a bit of theory and code the actual algorithm itself. The course doesn't just give you some package to use but presents you with code very easy to follow. The code is not optimized or written for maximum performance but for maximum readability. This means you can play around with it once you really understand it and speed it up. Thank you Dana for this amazing course. It has given me the confidence to code my own slightly more advanced algorithms from Sean Luke's book: Essential Metaheuristics. I feel the two are great companions." - Dylan, 5 star rating4) "It is a great introduction to Metaheuristics. The course deserves five stars for the overall information on this topic. The instructor is talented and knowledgeable about the optimization problems. I recommend the course for someone looking to solve an optimization problem." - Abdulaziz, 5 star rating5) "I still not finished the course, but until now, I am really satisfied with I've seen. THEORETICAL EXPLANATIONS: Dana is very didactic, before presenting the code she always briefly present the theory in a simple way, much easier to understand than books and journal papers explanations. Of course, it is necessary to complement this with other materials, but if you already have a theoretical base, it is just great! Dana, I loved your explanation about crossover and mutation! FOR BEGINNERS IN PYTHON: I am a beginner in Python and even in programming, so Dana's code helped me a lot to understand the meaning of each step and variable since she wrote a very readable code. GOOD TIME-MANAGEMENT: Dana presents the code already done but she explains what she has done in each step. Thus, in 5 minutes we can learn a lot, without being bored. I prefer this way of doing because I've done courses with teachers that do the code during the classes and we waste a lot of time fixing errors and bugs. She is objective and efficient on teaching, I like that. There are things not totally clear to me on courses, so I ask questions to Dana. She takes some days to give us an answer, but she replies anyway. I would appreciate an example of constraint handling for combinatorial problems." - Rachel, 4.5 star rating6) "Nice course that really does explain Metaheuristics in a very practical way. Highly recommended!" - David, 5 star rating