Description
Hill Climbing and Simulated Annealing AI Algorithms course. Search Engine Optimization Algorithms and Techniques More Techniques Artificial intelligence And data science are . There is no doubt that hill climbing and simulated annealing are the most well-regarded 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 mechanism of such AI problem solving techniques allows them to solve problems more effectively. This also allows them to adjust, modify and even design new algorithms for different projects. This course is the easiest way to understand the details How it works is hill climbing and annealing simulation. A deep understanding and mastery of these two algorithms will put you ahead of most data scientists. You will potentially have a better chance of joining a small group of AI professionals. Why learn optimization algorithms as a data scientist? Optimization is becoming popular every month in all industries with the main goal of improving revenue and reducing costs. Optimization algorithms of artificial intelligence techniques are very useful in different projects. You can use them to automate and optimize the process of solving challenging tasks. Who should learn about optimization? The first thing you need to learn is the mathematical models behind them. You won’t believe how easy and intuitive math models and equations are. This course begins with intuitive examples to introduce you to the most fundamental mathematical models of all hillclimbing and simulated annealing. There is no equation in this lesson without 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 minimal effort. It is also important to know what kind of problems can be solved with AI optimization algorithms. This course also shows different types of problems. There will also be several examples to practice how to solve such problems.
What you will learn in Hill Climbing and Simulated Annealing AI Algorithms course
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Search algorithms in artificial intelligence
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Hill climbing algorithm
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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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Traveling salesman problem
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Test functions for benchmark optimization algorithms
This course is suitable for people who
Description of the Hill Climbing and Simulated Annealing AI Algorithms course
- Publisher: Udemy
- teacher: 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
Hill Climbing and Simulated Annealing AI Algorithms course prerequisites
- Some programming background will definitely help to understand the coding videos
Course images
Sample video of the course
Installation guide
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English subtitle
Quality: 720p
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Volume
3.2 GB
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