Interpretable AI, Video Edition - Ajay Thampi (Make Complex AI Systems Interpretable)

Know the secrets of AI with the Free Course Interpretable AI, Video Edition - Ajay Thampi. This tutorial shares all the pragmatic strategies for explaining the AI model’s inner mechanisms, building trust with users, and meeting legal standards. Thampi is good at one thing: explaining why AI models are not intelligible and how we can handle the challenges.

Interpretable AI, Video Edition - Ajay Thampi This 200-minute video covers many topics, starting with simple white-box models like linear regression and decision trees and moving on to far more complex concepts like Partial Dependency Plots, LIME, SHAP, and Anchors, among others. Thampi provides the audience with what they require to minimize bias, prevent data leaks, and handle concept-drift issues when creating trustworthy and impartial AI systems. Both novice readers and specialists will find the presented concepts easy to understand and follow.

Unlike many contemporary books on the subject of artificial intelligence, Interpretable AI, Video Edition – Ajay Thampi provides various examples of AI transparency, which can be practically showcased, and various tips on how to make use of Python and open-source libraries that can be put in action in the majority of projects. If you need to enhance the interpretability of the AI models applied, meet the legal prerequisites like GDPR, or just plain people’s confidence in the results, the Interpretable AI, Video Edition - Ajay Thampi gives you all the information and the equipment necessary to succeed in this quickly-developing domain.

Interpretable AI, Video Edition - Ajay Thampi Table of Contents:

1. Part 1. Interpretability basics
2. Chapter 1. Introduction
3. Chapter 1. Types of machine learning systems
4. Chapter 1. Building Diagnostics+ AI
5. Chapter 1. Gaps in Diagnostics+ AI
6. Chapter 1. Building a robust Diagnostics+ AI system
7. Chapter 1. Interpretability vs. explainability
8. Chapter 1. What will I learn in this book?
9. Chapter 1. Summary
10. Chapter 2. White-box models
11. Chapter 2. Diagnostics+—diabetes progression
12. Chapter 2. Linear regression
13. Chapter 2. Decision trees
14. Chapter 2. Generalized additive models (GAMs)
15. Chapter 2. Looking ahead to black-box models
16. Chapter 2. Summary
17. Part 2. Interpreting model processing
18. Chapter 3. Model-agnostic methods: Global interpretability
19. Chapter 3. Tree ensembles
20. Chapter 3. Interpreting a random forest
21. Chapter 3. Model-agnostic methods: Global interpretability
22. Chapter 3. Summary
23. Chapter 4. Model-agnostic methods: Local interpretability
24. Chapter 4. Exploratory data analysis
25. Chapter 4. Deep neural networks
26. Chapter 4. Interpreting DNNs
27. Chapter 4. LIME
28. Chapter 4. SHAP
29. Chapter 4. Anchors
30. Chapter 4. Summary
31. Chapter 5. Saliency mapping
32. Chapter 5. Exploratory data analysis
33. Chapter 5. Convolutional neural networks
34. Chapter 5. Interpreting CNNs
35. Chapter 5. Vanilla backpropagation
36. Chapter 5. Guided backpropagation
37. Chapter 5. Other gradient-based methods
38. Chapter 5. Grad-CAM and guided Grad-CAM
39. Chapter 5. Which attribution method should I use?
40. Chapter 5. Summary
41. Part 3. Interpreting model representations
42. Chapter 6. Understanding layers and units
43. Chapter 6. Convolutional neural networks: A recap
44. Chapter 6. Network dissection framework
45. Chapter 6. Interpreting layers and units
46. Chapter 6. Summary
47. Chapter 7. Understanding semantic similarity
48. Chapter 7. Exploratory data analysis
49. Chapter 7. Neural word embeddings
50. Chapter 7. Interpreting semantic similarity
51. Chapter 7. Summary
52. Part 4. Fairness and bias
53. Chapter 8. Fairness and mitigating bias
54. Chapter 8. Fairness notions
55. Chapter 8. Interpretability and fairness
56. Chapter 8. Mitigating bias
57. Chapter 8. Datasheets for datasets
58. Chapter 8. Summary
59. Chapter 9. Path to explainable AI
60. Chapter 9. Counterfactual explanations
61. Chapter 9. Summary
62. Appendix A. Getting set up
63. Appendix A. Git code repository
64. Appendix A. Conda environment
65. Appendix A. Jupyter notebooks
66. Appendix A. Docker
67. Appendix B. PyTorch
68. Appendix B. Installing PyTorch
69. Appendix B. Tensors
70. Appendix B. Dataset and DataLoader
71. Appendix B. Modeling
72. Show and hide more

Who is this course for?

1. For data scientists and engineers familiar with Python and machine learning.

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