CORRECTION: At 10:56 we shouldn't divide by 4 to get the covariance, we should divide by 1+1+1+1/3, which is 10/3. That means the covariances are the following:
Var(x) = 1.056
Var(y) = 0.864
Cov(x,y) = 0.768
(Thank you Shivkumar Pippal!)

Mean, variance, covariance, and the covariance matrix for a dataset and a weighted dataset.

Announcement: Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML
40% discount code: serranoyt

0:00 Introduction
0:09 The covariance matrix
2:22 Average
3:23 X-variance
5:06 Problem: Same variances
7:59 Formulas
10:30 Center points

🚀 Small Language Models (SLMs): The Future of AI? 🤖

Most people think AI—like ChatGPT—needs huge servers and an internet connection. While that’s true for Large Language Models (LLMs), AI doesn’t have to be massive!

Introducing Small Language Models (SLMs)—AI that’s lightweight, efficient, and runs entirely offline. 📱💡

🔍 What You’ll Learn in This Video:
✅ SLMs vs. LLMs – What’s the difference?
✅ How SLMs work – Train them on your data for specific tasks.
✅ Real-world examples – AI for customer support, healthcare, and education.
✅ Why SLMs are the future – Privacy-friendly, low-energy, and cost-effective!

🌎 Why This Matters:
Unlike LLMs that require cloud computing, SLMs can run directly on your phone, laptop, or even a Raspberry Pi! No internet needed, no privacy concerns, and perfect for businesses, remote areas, and industries with strict data security requirements.

💡 SLMs Are Changing AI Forever!
• Instant AI-powered customer support 🏢
• Medical assistance in remote areas 🏥
• Personalized education tools for students 📚
• Energy-efficient AI for sustainability 🌱

This video covers about Small Language Models with a cast study

⏱ Chapter Timestamps
====================
00:00 - Intro
00:28 - What is Small Language Model
02:31 - Need for SLMs?
03:00 - Case Study: Offline Applications
05:06 - SLMs in the market

Join this channel by contributing to the community:
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📌 Related Links
=============
🔗CoPilot+PCs - https://www.youtube.com/watch?v=5JmkWJNng2I

📌 Related Playlist
================
🔗 AI Primer Playlist - https://www.youtube.com/playli....st?list=PLTyWtrsGknY
🔗Spring Boot Primer - https://www.youtube.com/playli....st?list=PLTyWtrsGknY
🔗Spring Cloud Primer - https://www.youtube.com/playli....st?list=PLTyWtrsGknY
🔗Spring Microservices Primer - https://www.youtube.com/playli....st?list=PLTyWtrsGknY
🔗Spring JPA Primer - https://www.youtube.com/playli....st?list=PLTyWtrsGknY
🔗Java 8 Streams - https://www.youtube.com/playli....st?list=PLTyWtrsGknY
🔗Spring Security Primer - https://www.youtube.com/playli....st?list=PLTyWtrsGknY

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🎬 Video Editing: FCP

---------------------------------------------------------------
🔥 Disclaimer/Policy:
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You are free to use the code samples in Github after forking and you can modify it for your own use.
All the videos posted here are copyrighted. You cannot re-distribute videos on this channel in other channels or platforms.
#SmallLanguageModel #LLMs #RAG

Get 20% off NVIDIA certification exams using the code "TechWithTim20" from here: https://nvda.ws/46Sehq8

Today we're diving deep into one of the hottest topics in AI right now. Building actual agents in Python. Not just chat bots to respond to your queries, but autonomous systems with memory goals and the ability to take actions in the world.

Want to make real money with coding? I share high-signal insights on careers, monetization, and leverage in my free newsletter. Join here and get my guide How to Make Money With Coding instantly: https://techwithtim.net/newsletter

⏳ Timestamps ⏳
00:00 | AI Agents
00:37 | NVIDIA Free AI Courses
01:38 | AI Agent Building Blocks
05:30 | Python Frameworks
09:01 | Python Tools
09:40 | AI Agent Patterns
13:15 | Picking Your Stack

Hashtags
#Nvidia #AiAgents #Python

Build Your First AI Agent In 15 Minutes (No Coding). Join our FREE community with all our prompts: https://www.skool.com/ai-launchpad/about

Chatling (FREE Trial): https://chatling.ai/

Chapters:
0:00 - Intro
1:47 - Setup Chatling
2:45 - Configure your agent
5:00 - Add knowledge base + data sources
6:49 - Configure actions
11:28 - Testing in playground
13:40 - Deploy to website
15:55 - Deploy to instagram
17:16 - Outro


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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!
----------------------------------------
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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🎥 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

----------------------------------------
🦫 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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⚖️ 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

#sponsored Build your first AI agent for free https://botpress.com/?utm_source=youtube&utm_medium=cpc&utm_campaign=aimaster&utm_term=buildchatgptagent

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Want to build AI agents but think you need to code? Wrong. In this video, I show you exactly how to create production-ready AI agents from scratch using three platforms: OpenAI Agent Builder, Botpress, and Zapier AI Agents.

You'll see the full workflow—from blank canvas to live agent—including:
✅ Knowledge base setup with RAG
✅ Real-time testing and deployment
✅ Multi-channel publishing (WhatsApp, Slack, web chat)
✅ Lead collection workflows
✅ Cost comparison and scaling strategies

🎯 WHAT YOU'LL LEARN:
• How to use ChatGPT to write agent prompts for you
• Setting up FAQ bots, customer support agents, and lead collectors
• Connecting knowledge bases (PDFs, websites, Notion)
• Deploying agents across WhatsApp, websites, and messaging platforms
• Why most platforms mark up LLM costs 30-50% (and how to avoid it)
• Which platform fits your use case: internal tools vs customer-facing agents

⚙️ PLATFORMS COVERED:
→ OpenAI Agent Builder: Fast setup, internal tools, shareable links
→ Zapier AI Agents: Automation workflows, trigger-based actions
→ Botpress: Production-grade agents, 190+ integrations, no LLM markup

📂 TIMESTAMPS:

0:00 — Build AI Agents Without Coding
1:09 — How to Build an AI Agent with OpenAI
3:49 — How to Build an AI Agent with Botpress
6:26 — Knowledge Base & RAG System
8:00 — Choosing Your LLM Model
8:41 — Testing & Deployment
9:11 — How to Build an AI Agent with Zapier
12:32 — Lead Collection Workflows in Botpress
15:32 — Deployment Across Channels
17:21 — Why Botpress for Production

🚀 START BUILDING TODAY:
The hardest part isn't building—it's starting. Pick one platform, sign up, and build your first agent this week. Start with a simple FAQ bot or lead collector, test it, deploy it, and scale from there.

💬 Built something cool? Tag me—I want to see it.

🔔 SUBSCRIBE for more AI tool comparisons, agent tutorials, and no-code automation workflows.

---

#AIAgents #NoCoding #ChatGPT #Botpress #Zapier #AIAutomation #CustomerSupport

Ready to become a certified watsonx Generative AI Engineer? Register now and use code IBMTechYT20 for 20% off of your exam → https://ibm.biz/BdnZTF

Learn more about Types of AI agents here → https://ibm.biz/BdnZTE

Can a drone deliver packages safely and efficiently? 🤖 Martin Keen breaks down the 5 types of AI agents—from reflex to learning models—and their role in robotics, decision-making, and automation. Learn how goal-driven and utility-based AI adapt to workflows and complex environments.

Intro - 0:00
Simple Reflex Agent - 0:50
Model-Based Reflex Agent - 2:49
Goal-Based AI Agent - 4:20
Utility Based AI Agent- 5:43
Learning AI Agent - 6:55
Use Cases - 8:22

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#aiagents #machinelearning #ai

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🖱️Links mentioned in video
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⏰Timestamps
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00:00 intro

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🐈‍⬛🐈‍⬛About me
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In recent years, the spotlight in AI has primarily been on large language models (LLMs) and emerging large multi-modal models (LMMs). Now, building on these tools, a new paradigm is emerging with the rise of AI agents and agentic reasoning, which are proving to be both cost-effective and powerful for building numerous new applications. As AI continues to evolve, data across all industries, particularly unstructured data such as text, images, video, and audio, is becoming more critical than ever. In this keynote session from BUILD 2024, Andrews Ng, Founder and Executive Chairman of Landing AI, explores the rise of AI, agents, and the growing role of unstructured data. He also discusses how this convergence will shape automation and application building across industries.

Andrew Ng will be a featured speaker at Snowflake Dev Day 2025. Join us on June 5 in San Francisco. Registration is free and open now: https://www.snowflake.com/en/summit/dev-day

Watch the Snowflake Summit 2024 Opening Keynote featuring Snowflake CEO Sridhar Ramaswamy and OpenAI CEO Sam Altman, moderated by Sarah Guo, founder and managing partner of Conviction here: https://youtu.be/gJf39VG87O8

Check out Andrew Ng speaking about AI agentic workflows and their potential for driving AI progress here:
👉 https://www.youtube.com/watch?v=q1XFm21I-VQ

Register to watch more BUILD on-demand here:
👉 https://www.snowflake.com/build/

Enroll in the "Introduction to Generative AI with Snowflake" course on Coursera:
👉 http://coursera.org/learn/intro-generative-ai-course-snowflake/?utm_medium=institutions&utm_source=snowflake&utm_campaign=yt-promo

❄Join our YouTube community❄ https://bit.ly/3lzfeeB

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Listen on:
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#Snowflake #DataCloud

Building a research multi agent system → https://goo.gle/41TYswp
Agent starter pack → https://goo.gle/4iZiz35
Human-in-the-Loop LangGraph app → https://goo.gle/4iyii7h

What is agentic AI and how can developers apply agentic AI in their applications? Welcome back to season 2 of Real Terms for AI with Googlers Aja Hammerly and Jason Davenport. In this video, Aja and Jason discuss agentic workflows, how agents are different from workflows, and when to use an agentic workflow. Follow along with code snippets in the Github link and help implement these concepts in many use cases.

Chapters:
0:00 - Intro
0:43 - What does agentic mean?
2:08 - Agentic workflow examples
3:28 - When to use agentic workflow or AI agent
4:33 - Wrap up

Watch more Real Terms for AI → https://goo.gle/AIwordsExplained
Subscribe to Google Cloud Tech → https://goo.gle/GoogleCloudTech

#GoogleCloud #GenerativeAI

Speakers: Aja Hammerly, Jason Davenport
Products Mentioned: Gemini, Gemma

For more information about Stanford's Artificial Intelligence programs visit: https://stanford.io/ai

In this webinar, you will gain an introduction to the concept of agentic language models (LMs) and their usage. You will learn about common limitations of LMs and agentic LM usage patterns, such as reflection, planning, tool usage, and iterative LM usage.

This online session will cover:
Overview of LMs
LM Usage and limitations
Retrieval Augmented Generation (RAG)
Tool usage
Agentic LMs
Agentic design patterns

About the speaker: Insop Song

Insop is a Principal Machine Learning Researcher at GitHub Next. Previously he worked at Microsoft, where he focused on leveraging machine learning and large language models to boost engineering productivity. His projects included fine-tuning open-source large language models with internal code and text, developing document assistance tools, and applying AI to various engineering tasks. He is currently a course developer as well as a course facilitator for Stanford Online’s professional AI program.


Chapters:
00:00 - Introduction
00:10 - Overview of the Talk
01:50 - Training Language Models
02:30 - Modeling Objectives
04:00 - Examples of Training Data Formatting
05:40 - Applications of Language Models
06:50 - Using API for Language Models
09:00 - Best Practices for Prompt Preparation
11:10 - Importance of Clear Instructions
13:40 - Reflection and Improvement Techniques
16:30 - Tool Usage and Function Calling
20:30 - Definition of Agentic Language Models
21:50 - Reasoning and Action in Agentic Models
24:00 - Example of a Customer Support AI Agent
29:20 - Summary of Applications
36:00 - Key Design Patterns in Agentic Models
44:00 - Summary of Agentic Language Model Usage
47:40 - Audience Q&A
50:00 - Addressing Ethical Considerations
54:50 - Getting Started with Language Models
57:00 - Resources for Staying Updated
58:20 - Closing Remarks

Read the full list of tips: http://builder.io/blog/claude-code

In this episode, I sit down with Professor Ras Mic for a beginner-friendly crash course on using Claude Code (and AI coding agents in general) without feeling overwhelmed by the terminal. We break down why your output is only as good as your inputs and how thinking in features + tests turns “vague app ideas” into real, shippable products. Was walks me through a better planning workflow using Claude Code’s Ask User Question Tool, which forces clarity on UI/UX decisions, trade-offs, and technical constraints before you build. We also talk about when not to use “Ralph” automation, why context windows matter, and how taste + audacity are the real differentiators in 2026 software.

Timestamps
00:00 – Intro
01:22 – Claude Code Best Practices
05:31 – Claude Code Plan Mode
09:30 – The Ask User Question Tool
14:52 – Don’t start with Ralph automation (get reps first)
16:36 – What are “Ralph loops” and why plans and documentation matter most
18:41 – Ras’s Ralph setup: progress tracking + tests + linting
23:48 – Tips & tricks: don’t obsess over MCP/skills/plugins
27:44 – Scroll-stopping software wins

Links Mentioned:

Ras's Ralphy AI Agent: https://startup-ideas-pod.link/ras-ralphy

Key Points

* Your results improve fast when you treat AI agents like junior engineers: clear inputs → clean outputs.
* The biggest unlock is planning in features + tests, not broad product descriptions.
* Claude Code’s Ask User Question Tool forces real clarity on workflow, UI/UX, costs, and technical decisions.
* If you haven’t shipped anything, don’t hide behind automation—build manually before using “Ralph.”
* Context management matters: long sessions can degrade quality, so restart earlier than you think.

Numbered Section Summaries

* The Real Reason People Get “AI Slop” I frame the episode around a simple idea: if you feed agents sloppy instructions, you’ll get sloppy output. Ras explains that models are now good enough that the failure mode is usually unclear inputs, not model quality.

* How To Think Like A Product Builder (Features First): Ras pushes a practical mindset: don’t describe “the product,” describe the _features_ that make the product real. If you can list the core features clearly, you can actually direct an agent to build them correctly.

* The Missing Piece: Tests Between Features: We talk about the shift from “generate code” to “build something serious.” The move is writing and running tests after each feature, so you don’t stack feature two on top of a broken feature one.

* Why Default Planning Mode Isn’t Enough: Ras shows the standard flow: open plan mode, ask Claude to write a PRD, and get a basic roadmap. The issue is it leaves too many assumptions—especially around UI/UX and workflow details.

* The Ask User Question Tool (The Planning Upgrade): This is the big unlock. Ras demonstrates how the Ask User Question Tool interrogates you with increasingly specific questions (workflow, cost handling, database/hosting, UI style, storage, etc.) so the plan becomes dramatically more precise.

* Spend Time Upfront Or Pay For It Later: We connect the dots: better planning reduces back-and-forth, reduces token burn, and prevents “I built the app but it’s not what I wanted.” The interview-style planning forces trade-offs early instead of late.

* Don’t Use Ralph Until You’ve Built Without It: Ras makes a strong case for reps: if you can’t ship something end-to-end yet, automation won’t save you—it’ll just move faster in the wrong direction. Build feature-by-feature manually first, then graduate to loops.

* Practical Tips: Context Discipline + Taste Wins: Ras shares a few operational habits: don’t obsess over tools like MCP/plugins, keep context usage under control, and restart sessions before quality degrades. We wrap on a bigger point: in 2026, “audacity + taste” is what makes software stand out.

The #1 tool to find startup ideas/trends - https://www.ideabrowser.com/
LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/
The Vibe Marketer - Resources for people into vibe marketing/marketing with AI: https://www.thevibemarketer.com/

FIND ME ON SOCIAL
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Youtube: https://www.youtube.com/@rasmic

MIT How to Speak, IAP 2018
Instructor: Patrick Winston
View the complete course: https://ocw.mit.edu/how_to_speak

Patrick Winston's How to Speak talk has been an MIT tradition for over 40 years. Offered every January, the talk is intended to improve your public speaking ability in critical situations by teaching you a few heuristic rules.

0:16 Introduction
3:11 Rules of Engagement
4:15 How to Start
5:38 Four Sample Heuristics
10:17 The Tools: Time and Place
13:24 The Tools: Boards, Props, and Slides
36:30 Informing: Promise, Inspiration, How To Think
41:30 Persuading: Oral Exams, Job Talks, Getting Famous
53:06 How to Stop: Final Slide, Final Words
56:35 Final Words: Joke, Thank You, Examples

This video has been dubbed using an artificial voice via https://aloud.area120.google.com to increase accessibility.

The traditional Chinese captions courtesy of 高嘉宏. Used with permission. The captions are reviewed and edited by MIT OpenCourseWare. The simplified Chinese captions are by MIT OpenCourseWare.

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

MIT 18.642 Topics in Mathematics with Applications in Finance, Fall 2024
Instructor: Jake Xia
View the complete course: https://ocw.mit.edu/courses/18....-642-topics-in-mathe
YouTube Playlist: https://www.youtube.com/playli....st?list=PLUl4u3cNGP6

In this video Jake Xia provides an overview of key financial concepts, including market structure, types of financial products like stocks, bonds, derivatives, and the roles of various market participants. He also introduces investment strategies, emphasizing the importance of understanding personal financial goals, risk management, and the use of mathematical models in pricing and trading, culminating in a practical trading game to apply these concepts.

License: Creative Commons BY-NC-SA
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More courses at https://ocw.mit.edu
Support OCW at http://ow.ly/a1If50zVRlQ

We encourage constructive comments and discussion on OCW’s YouTube and other social media channels. Personal attacks, hate speech, trolling, and inappropriate comments are not allowed and may be removed. More details at https://ocw.mit.edu/comments.