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[ DevCourseWeb com ] Oreilly - Optimization Algorithms, Video Edition
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001. Part 1. Deterministic search algorithms.mp4
002. Chapter 1. Introduction to search and optimization.mp4
003. Chapter 1. Going from toy problems to the real world.mp4
004. Chapter 1. Basic ingredients of optimization problems.mp4
005. Chapter 1. Well-structured problems vs. ill-structured problems.mp4
006. Chapter 1. Search algorithms and the search dilemma.mp4
007. Chapter 1. Summary.mp4
008. Chapter 2. A deeper look at search and optimization.mp4
009. Chapter 2. Classifying search and optimization algorithms.mp4
010. Chapter 2. Heuristics and metaheuristics.mp4
011. Chapter 2. Nature-inspired algorithms.mp4
012. Chapter 2. Summary.mp4
013. Chapter 3. Blind search algorithms.mp4
014. Chapter 3. Graph search.mp4
015. Chapter 3. Graph traversal algorithms.mp4
016. Chapter 3. Shortest path algorithms.mp4
017. Chapter 3. Applying blind search to the routing problem.mp4
018. Chapter 3. Summary.mp4
019. Chapter 4. Informed search algorithms.mp4
020. Chapter 4. Minimum spanning tree algorithms.mp4
021. Chapter 4. Shortest path algorithms.mp4
022. Chapter 4. Applying informed search to a routing problem.mp4
023. Chapter 4. Summary.mp4
024. Part 2. Trajectory-based algorithms.mp4
025. Chapter 5. Simulated annealing.mp4
026. Chapter 5. The simulated annealing algorithm.mp4
027. Chapter 5. Function optimization.mp4
028. Chapter 5. Solving Sudoku.mp4
029. Chapter 5. Solving TSP.mp4
030. Chapter 5. Solving a delivery semi-truck routing problem.mp4
031. Chapter 5. Summary.mp4
032. Chapter 6. Tabu search.mp4
033. Chapter 6. Tabu search algorithm.mp4
034. Chapter 6. Solving constraint satisfaction problems.mp4
035. Chapter 6. Solving continuous problems.mp4
036. Chapter 6. Solving TSP and routing problems.mp4
037. Chapter 6. Assembly line balancing problem.mp4
038. Chapter 6. Summary.mp4
039. Part 3. Evolutionary computing algorithms.mp4
040. Chapter 7. Genetic algorithms.mp4
041. Chapter 7. Introducing evolutionary computation.mp4
042. Chapter 7. Genetic algorithm building blocks.mp4
043. Chapter 7. Implementing genetic algorithms in Python.mp4
044. Chapter 7. Summary.mp4
045. Chapter 8. Genetic algorithm variants.mp4
046. Chapter 8. Real-valued GA.mp4
047. Chapter 8. Permutation-based GA.mp4
048. Chapter 8. Multi-objective optimization.mp4
049. Chapter 8. Adaptive GA.mp4
050. Chapter 8. Solving the traveling salesman problem.mp4
051. Chapter 8. PID tuning problem.mp4
052. Chapter 8. Political districting problem.mp4
053. Chapter 8. Summary.mp4
054. Part 4. Swarm intelligence algorithms.mp4
055. Chapter 9. Particle swarm optimization.mp4
056. Chapter 9. Continuous PSO.mp4
057. Chapter 9. Binary PSO.mp4
058. Chapter 9. Permutation-based PSO.mp4
059. Chapter 9. Adaptive PSO.mp4
060. Chapter 9. Solving the traveling salesman problem.mp4
061. Chapter 9. Neural network training using PSO.mp4
062. Chapter 9. Summary.mp4
063. Chapter 10. Other swarm intelligence algorithms to explore.mp4
064. Chapter 10. ACO metaheuristics.mp4
065. Chapter 10. ACO variants.mp4
066. Chapter 10. From hive to optimization.mp4
067. Chapter 10. Exploring the artificial bee colony algorithm.mp4
068. Chapter 10. Summary.mp4
069. Part 5. Machine learning-based methods.mp4
070. Chapter 11. Supervised and unsupervised learning.mp4
071. Chapter 11. Demystifying machine learning.mp4
072. Chapter 11. Machine learning with graphs.mp4
073. Chapter 11. Self-organizing maps.mp4
074. Chapter 11. Machine learning for optimization problems.mp4
075. Chapter 11. Solving function optimization using supervised machine learning.mp4
076. Chapter 11. Solving TSP using supervised graph machine learning.mp4
077. Chapter 11. Solving TSP using unsupervised machine learning.mp4
078. Chapter 11. Finding a convex hull.mp4
079. Chapter 11. Summary.mp4
080. Chapter 12. Reinforcement learning.mp4
081. Chapter 12. Optimization with reinforcement learning.mp4
082. Chapter 12. Balancing CartPole using A2C and PPO.mp4
083. Chapter 12. Autonomous coordination in mobile networks using PPO.mp4
084. Chapter 12. Solving the truck selection problem using contextual bandits.mp4
085. Chapter 12. Journey s end A final reflection.mp4
086. Chapter 12. Summary.mp4
087. Appendix A. Search and optimization libraries in Python.mp4
088. Appendix A. Mathematical programming solvers.mp4
089. Appendix A. Graph and mapping libraries.mp4
090. Appendix A. Metaheuristics optimization libraries.mp4
091. Appendix A. Machine learning libraries.mp4
092. Appendix A. Projects.mp4
093. Appendix B. Benchmarks and datasets.mp4
094. Appendix B. Combinatorial optimization benchmark datasets.mp4
095. Appendix B. Geospatial datasets.mp4
096. Appendix B. Machine learning datasets.mp4
097. Appendix B. Data folder.mp4
098. Appendix C. Exercises and solutions.mp4
099. Appendix C. Chapter 3 Blind search algorithms.mp4
100. Appendix C. Chapter 4 Informed search algorithms.mp4
101. Appendix C. Chapter 5 Simulated annealing.mp4
102. Appendix C. Chapter 6 Tabu search.mp4
103. Appendix C. Chapter 7 Genetic algorithm.mp4
104. Appendix C. Chapter 8 Genetic algorithm variants.mp4
105. Appendix C. Chapter 9 Particle swarm optimization.mp4
106. Appendix C. Chapter 10 Other swarm intelligence algorithms to explore.mp4
107. Appendix C. Chapter 11 Supervised and unsupervised learning.mp4
108. Appendix C. Chapter 12 Reinforcement learning.mp4
Bonus Resources.txt
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