Oreilly – Optimization Algorithms, Video Edition 2024-10

Oreilly – Optimization Algorithms, Video Edition 2024-10 Downloadly IRSpace

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Oreilly – Optimization Algorithms, Video Edition 2024-10

Optimization Algorithms, Video Edition. This comprehensive course teaches you how to use artificial intelligence algorithms to solve complex design, planning, and control problems. Whether you want to find the fastest route, determine the best price for your product, or optimize your resources, this course gives you the tools you need to solve these challenges. This course is perfect for those who are interested in learning complex concepts in a simple and practical way.

What you will learn:

Basic Search and Optimization Concepts: Deep understanding of the fundamental concepts of search and optimization
Deterministic and stochastic optimization techniques: Introduction to different methods for solving optimization problems.
Graph Search Algorithms: Learning Graph Search Algorithms
Path-based optimization algorithms: Introduction to algorithms that seek the best path
Evolutionary Computing: Learning Algorithms Inspired by Evolution
Crowd Intelligence: Introducing Algorithms Inspired by Collective Behavior
Machine Learning for Optimization: Using Machine Learning Methods to Solve Optimization Problems
Balancing exploration and exploitation: Finding the right balance between exploring the problem space and using existing information
Python Libraries: Learn Python libraries for implementing optimization algorithms.

This course is suitable for people who:

Are looking to learn modern AI techniques to solve real-world problems
They want to work in the field of design, planning, and control.
Are interested in learning optimization algorithms
Want to use Python’s powerful tools to implement algorithms

Course details Optimization Algorithms, Video Edition

Publisher: Oreilly
Instructor: Alaa Khamis
Training level: Beginner to advanced
Training duration: 15 hours and 54 minutes

Course headings

Part 1. Deterministic search algorithms
Chapter 1. Introduction to search and optimization
Chapter 1. Going from toy problems to the real world
Chapter 1. Basic ingredients of optimization problems
Chapter 1. Well-structured problems vs. ill-structured problems
Chapter 1. Search algorithms and the search dilemma
Chapter 1. Summary
Chapter 2. A deeper look at search and optimization
Chapter 2. Classifying search and optimization algorithms
Chapter 2. Heuristics and metaheuristics
Chapter 2. Nature-inspired algorithms
Chapter 2. Summary
Chapter 3. Blind search algorithms
Chapter 3. Graph search
Chapter 3. Graph traversal algorithms
Chapter 3. Shortest path algorithms
Chapter 3. Applying blind search to the routing problem
Chapter 3. Summary
Chapter 4. Informed search algorithms
Chapter 4. Minimum spanning tree algorithms
Chapter 4. Shortest path algorithms
Chapter 4. Applying informed search to a routing problem
Chapter 4. Summary
Part 2. Trajectory-based algorithms
Chapter 5. Simulated annealing
Chapter 5. The simulated annealing algorithm
Chapter 5. Function optimization
Chapter 5. Solving Sudoku
Chapter 5. Solving TSP
Chapter 5. Solving a delivery semi-truck routing problem
Chapter 5. Summary
Chapter 6. Taboo search
Chapter 6. Tabu search algorithm
Chapter 6. Solving constraint satisfaction problems
Chapter 6. Solving continuous problems
Chapter 6. Solving TSP and routing problems
Chapter 6. Assembly line balancing problem
Chapter 6. Summary
Part 3. Evolutionary computing algorithms
Chapter 7. Genetic algorithms
Chapter 7. Introducing evolutionary computation
Chapter 7. Genetic algorithm building blocks
Chapter 7. Implementing genetic algorithms in Python
Chapter 7. Summary
Chapter 8. Genetic algorithm variants
Chapter 8. Real-valued GA
Chapter 8. Permutation-based GA
Chapter 8. Multi-objective optimization
Chapter 8. Adaptive GA
Chapter 8. Solving the traveling salesman problem
Chapter 8. PID tuning problem
Chapter 8. Political districting problem
Chapter 8. Summary
Part 4. Swarm intelligence algorithms
Chapter 9. Particle swarm optimization
Chapter 9. Continuous PSO
Chapter 9. Binary PSO
Chapter 9. Permutation-based PSO
Chapter 9. Adaptive PSO
Chapter 9. Solving the traveling salesman problem
Chapter 9. Neural network training using PSO
Chapter 9. Summary
Chapter 10. Other swarm intelligence algorithms to explore
Chapter 10. ACO metaheuristics
Chapter 10. ACO variants
Chapter 10. From hive to optimization
Chapter 10. Exploring the artificial bee colony algorithm
Chapter 10. Summary
Part 5. Machine learning-based methods
Chapter 11. Supervised and unsupervised learning
Chapter 11. Demystifying machine learning
Chapter 11. Machine learning with graphs
Chapter 11. Self-organizing maps
Chapter 11. Machine learning for optimization problems
Chapter 11. Solving function optimization using supervised machine learning
Chapter 11. Solving TSP using supervised graph machine learning
Chapter 11. Solving TSP using unsupervised machine learning
Chapter 11. Finding a convex hull
Chapter 11. Summary
Chapter 12. Reinforcement learning
Chapter 12. Optimization with reinforcement learning
Chapter 12. Balancing CartPole using A2C and PPO
Chapter 12. Autonomous coordination in mobile networks using PPO
Chapter 12. Solving the truck selection problem using contextual bandits
Chapter 12. Journey’s end: A final reflection
Chapter 12. Summary
Appendix A. Search and optimization libraries in Python
Appendix A. Mathematical programming solvers
Appendix A. Graph and mapping libraries
Appendix A. Metaheuristics optimization libraries
Appendix A. Machine learning libraries
Appendix A. Projects
Appendix B. Benchmarks and datasets
Appendix B. Combinatorial optimization benchmark datasets
Appendix B. Geospatial datasets
Appendix B. Machine learning datasets
Appendix B. Data folder
Appendix C. Exercises and solutions
Appendix C. Chapter 3: Blind search algorithms
Appendix C. Chapter 4: Informed search algorithms
Appendix C. Chapter 5: Simulated annealing
Appendix C. Chapter 6: Tabu search
Appendix C. Chapter 7: Genetic algorithm
Appendix C. Chapter 8: Genetic algorithm variants
Appendix C. Chapter 9: Particle swarm optimization
Appendix C. Chapter 10: Other swarm intelligence algorithms to explore
Appendix C. Chapter 11: Supervised and unsupervised learning
Appendix C. Chapter 12: Reinforcement learning

Course images

Optimization Algorithms, Video Edition

Sample course video

Installation Guide

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Subtitles: None

Quality: 1080p

Download link

Download Part 1 – 1 GB

Download Part 2 – 1 GB

Download Part 3 – 588 MB

File(s) password: www.downloadly.ir

File size

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Oreilly – Optimization Algorithms, Video Edition 2024-10