Bayesian Optimization in Action, Video Edition
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11:55:40
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001. Chapter 1. Introduction to Bayesian optimization.mp4
15:49
002. Chapter 1. Introducing Bayesian optimization.mp4
27:30
003. Chapter 1. What will you learn in this book.mp4
01:34
004. Chapter 1. Summary.mp4
01:41
005. Part 1. Modeling with Gaussian processes.mp4
01:32
006. Chapter 2. Gaussian processes as distributions over functions.mp4
09:04
007. Chapter 2. Modeling correlations with multivariate Gaussian distributions and Bayesian updates.mp4
19:25
008. Chapter 2. Going from a finite to an infinite Gaussian.mp4
09:09
009. Chapter 2. Implementing GPs in Python.mp4
21:40
010. Chapter 2. Exercise.mp4
02:22
011. Chapter 2. Summary.mp4
01:55
012. Chapter 3. Customizing a Gaussian process with the mean and covariance functions.mp4
10:19
013. Chapter 3. Incorporating what you already know into a GP.mp4
03:20
014. Chapter 3. Defining the functional behavior with the mean function.mp4
21:51
015. Chapter 3. Defining variability and smoothness with the covariance function.mp4
18:24
016. Chapter 3. Exercise.mp4
02:27
017. Chapter 3. Summary.mp4
01:44
018. Part 2. Making decisions with Bayesian optimization.mp4
02:35
019. Chapter 4. Refining the best result with improvement-based policies.mp4
24:06
020. Chapter 4. Finding improvement in BayesOpt.mp4
25:54
021. Chapter 4. Optimizing the expected value of improvement.mp4
07:35
022. Chapter 4. Exercises.mp4
08:00
023. Chapter 4. Summary.mp4
02:16
024. Chapter 5. Exploring the search space with bandit-style policies.mp4
14:33
025. Chapter 5. Being optimistic under uncertainty with the Upper Confidence Bound policy.mp4
16:51
026. Chapter 5. Smart sampling with the Thompson sampling policy.mp4
18:23
027. Chapter 5. Exercises.mp4
04:46
028. Chapter 5. Summary.mp4
02:20
029. Chapter 6. Using information theory with entropy-based policies.mp4
28:58
030. Chapter 6. Entropy search in BayesOpt.mp4
13:10
031. Chapter 6. Exercises.mp4
06:38
032. Chapter 6. Summary.mp4
02:06
033. Part 3. Extending Bayesian optimization to specialized settings.mp4
03:18
034. Chapter 7. Maximizing throughput with batch optimization.mp4
13:25
035. Chapter 7. Computing the improvement and upper confidence bound of a batch of points.mp4
27:32
036. Chapter 7. Exercise 1 Extending TS to the batch setting via resampling.mp4
02:55
037. Chapter 7. Computing the value of a batch of points using information theory.mp4
11:24
038. Chapter 7. Exercise 2 Optimizing airplane designs.mp4
06:36
039. Chapter 7. Summary.mp4
03:18
040. Chapter 8. Satisfying extra constraints with constrained optimization.mp4
13:41
041. Chapter 8. Constraint-aware decision-making in BayesOpt.mp4
12:35
042. Chapter 8. Exercise 1 Manual computation of constrained EI.mp4
03:29
043. Chapter 8. Implementing constrained EI with BoTorch.mp4
08:49
044. Chapter 8. Exercise 2 Constrained optimization of airplane design.mp4
02:30
045. Chapter 8. Summary.mp4
01:59
046. Chapter 9. Balancing utility and cost with multifidelity optimization.mp4
13:02
047. Chapter 9. Multifidelity modeling with GPs.mp4
20:38
048. Chapter 9. Balancing information and cost in multifidelity optimization.mp4
17:54
049. Chapter 9. Measuring performance in multifidelity optimization.mp4
09:30
050. Chapter 9. Exercise 1 Visualizing average performance in multifidelity optimization.mp4
04:46
051. Chapter 9. Exercise 2 Multifidelity optimization with multiple low-fidelity approximations.mp4
04:12
052. Chapter 9. Summary.mp4
03:51
053. Chapter 10. Learning from pairwise comparisons with preference optimization.mp4
13:23
054. Chapter 10. Formulating a preference optimization problem and formatting pairwise comparison data.mp4
08:36
055. Chapter 10. Training a preference-based GP.mp4
09:29
056. Chapter 10. Preference optimization by playing king of the hill.mp4
08:13
057. Chapter 10. Summary.mp4
02:12
058. Chapter 11. Optimizing multiple objectives at the same time.mp4
07:02
059. Chapter 11. Finding the boundary of the most optimal data points.mp4
18:09
060. Chapter 11. Seeking to improve the optimal data boundary.mp4
12:51
061. Chapter 11. Exercise Multiobjective optimization of airplane design.mp4
02:53
062. Chapter 11. Summary.mp4
02:18
063. Part 4. Special Gaussian process models.mp4
01:22
064. Chapter 12. Scaling Gaussian processes to large datasets.mp4
16:11
065. Chapter 12. Automatically choosing representative points from a large dataset.mp4
22:32
066. Chapter 12. Optimizing better by accounting for the geometry of the loss surface.mp4
09:48
067. Chapter 12. Exercise.mp4
03:53
068. Chapter 12. Summary.mp4
02:31
069. Chapter 13. Combining Gaussian processes with neural networks.mp4
09:10
070. Chapter 13. Capturing similarity within structured data.mp4
13:38
071. Chapter 13. Using neural networks to process complex structured data.mp4
16:03
072. Chapter 13. Summary.mp4
02:05
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- language english
- Training sessions 72
- duration 11:55:40
- Release Date 2024/06/14
