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Correction (10:26). The probabilities are wrong. The correct ones are here:
For Die 1: 0.4^4 * 0.2^2 * 0.1^1 * 0.1^1 * 0.2^2
For Die 2: 0.4^4 * 0.1^2 * 0.2^1 * 0.2^1 * 0.1^2
For Die 3: 0.1^4 * 0.2^2 * 0.4^1 * 0.2^1 * 0.1^2

Kullback Leibler (KL) divergence is a way to measure how far apart two distributions are.
In this video, we learn KL-divergence in a simple way, using a probability game with dice.

Shannon entropy and information gain: https://www.youtube.com/watch?v=9r7FIXEAGvs&t=1066s&pp=ygUic2hhbm5vbiBlbnRyb3B5IGluZm9ybWF0aW9uIHRoZW9yeQ%3D%3D

Grokking Machine Learning book: www.manning.com/books/grokking-machine-learning
40% discount code: serranoyt

Check out Kimi here! https://www.kimi.com/?utm_campaign=TR_grOjaW6m&utm_content=&utm_medium=Youtube&utm_source=CH_kEMBez3l&utm_term=

Timestamps ⏰
00:00 AI agents course!
01:13 Basics of AI Agents - What is an AI Agent?
02:58 What tasks are agents good for?
05:47 Spectrum of Autonomy
06:45 Context Engineering
07:13 Task Decomposition
07:59 Demo: No-Code Agent System
09:22 Intermediate AI Agents - Measuring Performance
10:39 Memory
11:42 Guardrails
12:50 Reflection
13:56 Tool Use
15:51 Designing Good Tools
16:56 Planning
19:05 Multi-Agent Collaboration
20:38 Multi-Agent System Design - Roles
21:25 Multi-Agent System Design - Communication Patterns
23:09 Multi-Agent System Design - Communication Pitfalls
23:44 Multi-Agent System Design - Best Practices
25:02 Demo: Multi-Agent System in Python
26:14 Advanced AI Agents - Advanced Task Decomposition
29:02 Improving Performance
30:26 Reducing Latency
31:16 Reducing Cost
32:39 Observability and Monitoring
35:10 Security
37:17 Bonus Resource!
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Agentic System Design:
https://gerred.github.io/build....ing-an-agentic-syste

The slides and examples were adapted from a few different courses, including:
https://www.deeplearning.ai/co....urses/design-develop
https://learn.deeplearning.ai/....courses/agentic-ai/i
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Want to become an AI Engineer? Download my AI Engineering Skills Checklist here: https://www.gratitudedriven.co....m/c/ai-engineering-c

💬 Want to talk 1:1? Book time to chat with me here: https://topmate.io/marina_wyss

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☕ If you'd like to support my work, you can buy me a coffee (thank you!): https://ko-fi.com/marinawyss
----------------------------------------
🎥 Other videos you might like:

AI Engineering in 76 Minutes (Complete Course/Speedrun!)
https://www.youtube.com/watch?v=JV3pL1_mn2M&t=2s

AI Engineering: A *Realistic* Roadmap for Beginners
https://www.youtube.com/watch?v=dbUIjFXIpis

4 *Real* Machine Learning Projects That Get You Hired - No More Tutorials!
https://www.youtube.com/watch?v=MFSFcPsMsuE

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🦫 About me
I am a Senior Applied Scientist (basically, a blend of Data Scientist/Machine Learning Engineer) at Twitch/Amazon. Outside of my full-time job I'm a 1:1 career coach for people looking to break into the field, with a focus on those from non-traditional backgrounds.

I’m also a Certified Personal Trainer, always busy with too many interests, and really, deeply happy with my life. I hope to be able to help others achieve these things, too.
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✉️ Contact
Instagram: https://www.instagram.com/marina.wyss/
Twitter/X: https://x.com/iammarinawyss
TikTok: https://www.tiktok.com/@gratitudedriven
Leave me a comment here on YouTube!
Business email: [email protected]

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⚖️ Disclaimer
The views and opinions expressed in this video are my own and do not reflect the official policy or position of Twitch/Amazon or any other company I have worked for. All advice and insights shared here are based on my personal experiences and should be considered as such.

Thank you to Kimi for sponsoring this video!

This description may contain affiliate links. If you make a purchase I may make a small commission at no cost to you.

#AI #AIEngineering #AIAgents

MIT 22.01 Introduction to Nuclear Engineering and Ionizing Radiation, Fall 2016
Instructor: Michael Short
View the complete course: https://ocw.mit.edu/22-01F16
YouTube Playlist: https://www.youtube.com/playli....st?list=PLUl4u3cNGP6

A brief summary of the discovery of forms of ionizing radiation up to the 1932 discovery of the neutron. We introduce mass-energy equivalence for the first time and explain how these cutting-edge experiments (for their time) conclusively proved the existence of high-energy, ionizing radiation.

License: Creative Commons BY-NC-SA
More information at https://ocw.mit.edu/terms
More courses at https://ocw.mit.edu

(March 29, 2010) Stanford professor Robert Sapolsky gave the opening lecture of the course entitled Human Behavioral Biology and explains the basic premise of the course and how he aims to avoid categorical thinking.

Stanford University
http://www.stanford.edu

Stanford Department of Biology
http://biology.stanford.edu/

Stanford University Channel on YouTube
http://www.youtube.com/stanford

Deep learning has shown remarkable success in processing and understanding unstructured data like text and images. In this module, we will explore how deep neural networks can be leveraged to build intelligent systems in the domains of natural language processing and computer vision. Get ready to dive into the exciting world of deep learning on text and images!

We will start with the agenda and course overview. Then, we will review some key machine learning concepts, including regression, optimization, regularization, and text and data representation.

--------------------

This video is part of Machine Learning University’s open-source "Application of Deep Learning to Text and Image Data" series, part of our Fundamentals of Machine Learning content. Access the full set of lessons, hands-on labs, and Jupyter Notebooks here:

🔗 GitHub Repository: https://github.com/aws-mlu/aws....-mlu-eep-traditional
🔗 Watch More MLU Videos: https://www.youtube.com/@machinelearninguniversity

Machine Learning University provides free, open-source AI/ML educational content for educators. New content is released regularly — subscribe to stay updated.