mjmaher987/Artificial-Intelligence
Artificial Intelligence + Deep Learning
This project provides practical implementations of various artificial intelligence algorithms. It helps you understand and experiment with how AI solves problems like game strategies, finding optimal routes, and learning through trial and error. By running these examples, you can see how different AI approaches take in problem definitions (like game rules, city locations, or desired states) and output optimal moves, efficient paths, or learned behaviors. This is ideal for students, researchers, or anyone learning about core AI concepts.
No commits in the last 6 months.
Use this if you are an AI/ML student, researcher, or educator looking for hands-on examples of classic AI algorithms like adversarial search, local search, informed search, and reinforcement learning.
Not ideal if you are looking for a ready-to-use application or a high-performance library to integrate advanced AI into a product.
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Jupyter Notebook
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Last pushed
Nov 19, 2023
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