Transformer Neural Networks are the heart of pretty much everything exciting in AI right now. ChatGPT, Google Translate and many other cool things, are based on Transformers. This StatQuest cuts through all the hype and shows you how a Transformer works, one-step-at-a time.

NOTE: If you're interested in learning more about Backpropagation, check out these 'Quests:
The Chain Rule: https://youtu.be/wl1myxrtQHQ
Gradient Descent: https://youtu.be/sDv4f4s2SB8
Backpropagation Main Ideas: https://youtu.be/IN2XmBhILt4
Backpropagation Details Part 1: https://youtu.be/iyn2zdALii8
Backpropagation Details Part 2: https://youtu.be/GKZoOHXGcLo

If you're interested in learning more about the SoftMax function, check out:
https://youtu.be/KpKog-L9veg

If you're interested in learning more about Word Embedding, check out: https://youtu.be/viZrOnJclY0

If you'd like to learn more about calculating similarities in the context of neural networks and the Dot Product, check out:
Cosine Similarity: https://youtu.be/e9U0QAFbfLI
Attention: https://youtu.be/PSs6nxngL6k

If you'd like to support StatQuest, please consider...
Patreon: https://www.patreon.com/statquest
...or...
YouTube Membership: https://www.youtube.com/channe....l/UCtYLUTtgS3k1Fg4y5

...buying my book, a study guide, a t-shirt or hoodie, or a song from the StatQuest store...
https://statquest.org/statquest-store/

...or just donating to StatQuest!
paypal: https://www.paypal.me/statquest
venmo: @JoshStarmer

Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter:
https://twitter.com/joshuastarmer

0:00 Awesome song and introduction
1:26 Word Embedding
7:30 Positional Encoding
12:53 Self-Attention
23:37 Encoder and Decoder defined
23:53 Decoder Word Embedding
25:08 Decoder Positional Encoding
25:50 Transformers were designed for parallel computing
27:13 Decoder Self-Attention
27:59 Encoder-Decoder Attention
31:19 Decoding numbers into words
32:23 Decoding the second token
34:13 Extra stuff you can add to a Transformer

#StatQuest #Transformer #ChatGPT

PRESENTING THE FULL VIDEO SONG 'MILLIONAIRE' BY YO YO HONEY SINGH, FROM THE MOST AWAITED ALBUM OF THE YEAR 'GLORY'.

GULSHAN KUMAR & T-SERIES PRESENTS
BHUSHAN KUMAR'S 'MILLIONAIRE'

#YOYOHONEYSINGH #MILLIONAIRE #GLORY

MAKE YOUR SHORTS ON "MILLIONAIRE" NOW👉 https://youtube.com/source/yleXPEPJIgI/shorts

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CAST - YO YO HONEY SINGH & HEERA SOHHAL
SINGER: YO YO HONEY SINGH 
MUSIC PRODUCER: YO YO HONEY SINGH 
PRIMARY LANGUAGE(S): PUNJABI & ENGLISH 
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LYRICS WRITER: LEO GREWAL 
MUSIC COMPOSER: YO YO HONEY SINGH
RECORDED AT MOSIQI STUDIOS AND LPME STUDIOS, DUBAI.
MASTERED BY - HOMMIE DILLIWALA
MIXING BY - VINOD VERMA
MUSIC LABEL - T-SERIES

DIRECTOR  : TEJI SANDHU
DOP : SIMON STOLLAND
PRODUCTION :TEN PLUS ONE PRODUCTION UK
YYHS STYLING- AKANKSHA BHAKOO
MUA - JIND KAUR AND TRIPTI
ASSISTANT DIRECTOR: KARANDEEP SINGH AND SOURAV DHEER
EDIT : SAMUEL MATON
COLOURIST: MISHA KIM
LINE PRODUCER - MANDEEP HUNDAL

DIGITAL STRATEGY : RAJDEEP MAYER
DIGITAL MARKETING & PROMOTIONS : RDM MEDIA

Download Song Beat: https://bit.ly/3Cjh24R

___________________________________
Enjoy & stay connected with us!
👉 Subscribe to T-Series: https://youtube.com/tseries
👉 Like us on Facebook: https://www.facebook.com/tseriesmusic
👉 Follow us on X: https://twitter.com/tseries
👉 Follow us on Instagram: https://instagram.com/tseries.official

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Get the guide to GAI, learn more → https://ibm.biz/BdKTbF
Learn more about the technology → https://ibm.biz/BdKTbX

Join Cedric Clyburn as he explores the differences and use cases of Retrieval Augmented Generation (RAG) and fine-tuning in enhancing large language models. This video covers the strengths, weaknesses, and common applications of both techniques, and provides insights on how to choose between them using machine learning and natural language processing principles

AI news moves fast. Sign up for a monthly newsletter for AI updates from IBM → https://www.ibm.com/account/re....g/us-en/signup?formi

#AI #LargeLanguageModels #FineTuning #RAG #ReinforcementLearning #MachineLearning #NaturalLanguageProcessing

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This full course on AI agents is segmented into three chapters: foundational understanding of AI agents, hands-on tutorials for building various AI use cases, and strategies for monetization. You’ll gain insights into the anatomy of AI agents, practical steps for creating them using no-code platforms, and real-world applications to seize the growing opportunities in AI.

Timestamps:
0:00 - What We’re Covering
2:39 - Why Learn to Build AI Agents?
5:39 - What Are AI Agents?
6:40 - Chatbot or Agent?
8:44 - Anatomy of an AI Agent
12:34 - The Three Ingredients
13:58 - The Web, APIS, and Tools Explained
17:04 - Anatomy of a Tool
18:40 - Schemas: API Instruction Manuals
23:00 - Advanced Tools Use
26:11 - Conversational or Automated Agents
29:23 - Real-World Applications
32:39 - Foundations Summary
35:00 - What We’re Building
38:34 - Build 1
1:11:12 - Build 2
1:47:44 - Build 3
3:01:29 - Build 4
3:35:29 - The Real Opportunity
3:39:47 - Three Ways to Win
3:41:30 - Extending Your Knowledge Gap
3:45:49 - Getting Your First Clients
3:48:46 - Next Steps

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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:
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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

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New Claude Code Update is INSANE! (AI Coding Revolution)

Anthropic just released a massive update to Claude Code Desktop that transforms your computer into an autonomous AI coding machine. Discover how it handles everything from live app previews to automated PR merges while streamlining your entire development workflow. Learn why this update is a game-changer for developers and how to start using it today.

00:00 - Intro: The Massive Claude Update
01:04 - Live App Previews & Auto-Fixing
02:07 - Automated Local Code Reviews
02:47 - CI Failure Handling & Auto-Fix
03:26 - PR Monitoring & Auto-Merge
04:21 - Session Mobility Explained
04:48 - The Future of AI Development
06:59 - How to Get Started with Claude Code

MIT 18.06 Linear Algebra, Spring 2005
Instructor: Gilbert Strang
View the complete course: http://ocw.mit.edu/18-06S05
YouTube Playlist: https://www.youtube.com/playli....st?list=PLE7DDD91010

1. The Geometry of Linear Equations

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

This video introduces the fundamentals of protein design and summarizes the trajectory of the course with a focus on (1) the foundational biochemistry that protein structure prediction and design hinge on and (2) deep learning / machine learning principles overview.

Video from the Rosetta Commons PPI Workshop (February 2025)
Video Instructor: Amrita Nallathambi (UNC Chapel Hill)

Credits:
Instructor: Amrita Nallathambi
Teaching Assistants: Yehlin Cho, Cyrus Haas, and Matthew Hvasta,
RC Leadership and NSF Sponsor Grant PIs: Julia Koehler Leman & Jeffrey Gray
RC Education Director: Ashley Vater
Videographer: Canyon Florey
Rosetta Workshop Participants

00:00 - Introduction
00:20 - Deep Learning Revolution for Proteins
01:50 - The Transformer
03:38 - AlphaFold2 Overview
06:22 - AlphaFold2 Inputs
09:48 - AlphaFold2 Outputs
13:29 - Graph Neural Networks
16:24 - ProteinMPNN Loss
17:35 - Diffusion models
18:30 - RFDiffusion
19:52 - Inputs and Outputs
20:43 - Potentials