Description
Course “Hill Climbing” and “Simulated Annealing AI algorithms”. These are mostly search algorithms and search engine optimization techniques artificial intelligence And Data Science Techniques. There is no doubt that Hill Climbing and Simulated Annealing are the most respected and widely used AI search techniques. Many scientists and doctors use search and optimization algorithms without understanding their internal structure. However, understanding the internal structure and mechanisms of such AI problem-solving techniques enables them to solve problems more effectively. This also enables them to adapt, modify and even design new algorithms for different projects. This course is the easiest way to understand the details how Hill Climbing and Simulated Annealing work. Having a deep understanding and mastery of these two algorithms will put you ahead of most data scientists and may give you a better chance of joining a small group of AI experts. Why learn optimization algorithms as a data scientist? Optimization is becoming more popular every month in all industries, with the main goal of increasing sales and reducing costs. Optimization algorithms of artificial intelligence techniques are very useful in various projects. They allow you to automate and optimize the process of solving demanding tasks. Who should learn about optimization? The first thing you need to learn is the mathematical models behind them. You won’t believe how simple and intuitive mathematical models and equations are. This course starts with intuitive examples to introduce you to the most basic mathematical models of all hill climbing and simulated annealing procedures. There is no equation in this lesson without detailed explanation and visual examples. If you hate math, then sit back, relax and enjoy the videos to learn the math behind neural networks with minimal effort. It is also important to know what problems can be solved using AI optimization algorithms. This course also shows different types of problems. There will also be several examples to practice solving such problems.
What you will learn in the Hill Climbing and Simulated Annealing AI Algorithms course
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Search algorithms in artificial intelligence
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Simulated annealing algorithm
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Problem solving using search techniques
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Search and optimization in artificial intelligence
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Problem with traveling salesmen
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Test functions for benchmark optimization algorithms
This course is suitable for people who
Course Description Hill Climbing and Simulated Annealing AI Algorithms
- Editor: Udemy
- Lecturer: Seyedali Mirjalili
- Training level: beginner to advanced
- Training duration: 3 hours and 29 minutes
- Number of courses: 13
Course topics Hill Climbing and Simulated Annealing AI algorithms
Course Prerequisites for Hill Climbing and Simulated Annealing AI Algorithms
- Some programming knowledge will definitely help in understanding the coding videos
Course pictures
Sample video of the course
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English subtitles
Quality: 720p
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3.2GB