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所在平台: Udemy |
课程主页: https://www.udemy.com/course/hill-climbing-and-simulated-annealing-ai-algorithms/
课程评论：没有评论
课程名称：爬山与模拟退火AI算法 课程概述： 爬山与模拟退火是最受欢迎的人工智能搜索技术，广泛应用于数据科学和优化问题。本课程旨在帮助学生深入理解这两种算法的内部结构和机制，使他们能够更高效地解决问题，并为不同项目调整、修改或设计新算法。通过对这些优化算法的掌握，学员在数据科学领域将占据优势，更有机会成为高薪AI专家。 学习优化算法的意义： 在各行各业中，优化越来越受到关注，其主要目的是提高收益和降低成本。优化算法在各种项目中非常实用，能够帮助自动化和优化复杂任务的解决过程。 学习内容： 课程从直观的示例入手，讲解爬山算法，并展示其基本程序和应用。接下来的部分涵盖了AI优化领域的相关术语，然后我们将带领学员设计基于爬山算法的模拟退火算法。最后，学员将在不同的问题上实施并应用这两种算法，包括测试函数和旅行商问题。 课程适合人群： 本课程为AI优化与搜索的入门课程，无需具备人工智能、机器学习或数据科学的先前知识。但基本的编程理解，特别是Matlab，会对跟上编程视频有所帮助。如果只想学习数学模型和问题解决过程，可以跳过编程视频。 讲师介绍： 课程由一位在优化与AI领域领先的研究人员主讲，拥有超过150篇出版物。作为影响力卓著的研究者，讲师曾为大型公司提供咨询，如Facebook和Google。课程旨在简化学习过程，帮助对爬山和模拟退火感兴趣的学员。 课程保障： 课程提供30天的全额退款保证，若对课程不满意可随时申请退款。若您有兴趣，请点击“添加到购物车”按钮，现在就开始学习吧！
Search Algorithms and Optimization techniques are the engines of most Artificial Intelligence techniques and Data Science. There is no doubt that Hill Climbing and Simulated Annealing are the most well-regarded and widely used AI search techniques.A lot of scientists and practitioners use search and optimization algorithms without understanding their internal structure. However, understanding the internal structure and mechanism of such AI problem-solving techniques will allow them to solve problems more efficiently. This also allows them to tune, tweak, and even design new algorithms for different projects.This course is the easiest way to understand how Hill Climbing and Simulated Annealing work in detail. An in-depth understanding of these two algorithms and mastering them puts you ahead of a lot of data scientists. You will potentially have a higher chance of joining a small pool of well-paid AI experts.Why learn optimization algorithms as a Data Scientist?Optimization is getting popular in all industries every single month with the main purpose of improving revenue and decreasing costs. Optimization algorithms are extremely practical AI techniques in different projects. You can use them to automate and optimize the process of solving challenging tasks.What does anyone need to learn about optimization?The first thing you need to learn is the mathematical models behind them. You cannot believe how easy and intuitive the mathematical models and equations are. This course starts with intuitive examples to take you through the most fundamental mathematical models of all both Hill Climbing and Simulated Annealing. There is no equation in this course without an in-depth explanation and visual examples. If you hate math, then sit back, relax, and enjoy the videos to learn the math behind Neural Networks with minimum efforts.It is also important to know what types of problems can be solved with AI optimization algorithms. This course shows different types of problems as well. There will be several examples to practice how to solve such problems as well.What does this course cover?As discussed above, this course starts straight up with an intuitive example to see what a Hill Climbing is as one of the most fundamental AI problem-solving approaches. After learning how easy and simple the inspiration and algorithms of Hill Climbing are, you will see how it performs in action live.The second part of this course covers terminologies in the field of AI Optimization. In the third part, we will work with you on the process of designing Simulated Annealing using Hill Climbing. In the first three parts of this course, you master how the inspiration, theory, mathematical models, and algorithms of both Hill Climbing and Simulated Annealing algorithms.In the last part of the course, we will implement both algorithms and apply them to some problems including a wide range of test functions and Travelling Salesman Problems.By the end of this course, you will have a comprehensive understanding of Hill Climbing and Simulated Annealing and able to easily use them in your project. You can analyze, tune, and improve the performance of both techniques based on your project too.Does this course suit you?This course is an introduction to optimization and search in AI, so you need absolutely no prior knowledge in Artificial Intelligence, Machine Learning, or data science. However, you need to have a basic understanding of programming preferably in Matlab to easily follow the coding video. If you just want to learn the mathematical model and the problem-solving process using the two algorithms, you can then skip the coding videos.Who is the instructor?I am a leading researcher in the field of Optimization and AI. I have more than 150 publications including 100 journal articles, five books, and 20 conference papers. These publications have been cited over 15,000 times around the world. I was named as one of the most influential researchers in AI in 2019 by the Web of Science, the most well-regarded indexing organization in academia.As a leading researcher in this field with over 15 years of experience, I have prepared this course to make everything easy for those interested in AI search and optimization. I have been counseling big companies like Facebook and Google in my career too. I am also a star-rising Udemy instructor with more than 5000 students and 1200 5-star reviews, I have designed and developed this course to facilitate the process of learning Hill Climbing and Simulated Annealing for those who are interested in this area. You will have my full support throughout your learning journey in this course.There is no RISK!I have some preview videos, so make sure to watch them see if this course is for you. This course comes with a full 30-day money-back guarantee, which means that if you are not happy after your purchase, you can get a 100% refund no question.What are you waiting for?Enroll now using the "Add to Cart" button on the right and get started today.
