Videos de GenAI for Data Engineers
Videos etiquetados con "GenAI for Data Engineers"
GenAI for Data Engineers 2 videos
✅ How Transformers Work - Attention Explained Step by Step | Chapter 06
How do transformers actually work inside an LLM? This video breaks down the full transformer architecture - attention, encoder vs decoder, and next-token prediction - in plain English, no scary math required. Transformers are the secret sauce behind GPT, Claude, and every frontier model. By the end of this video you'll be able to look at the "Attention Is All You Need" diagram and understand exactly what every block does and why it's there. ===== In this video, you will learn ===== • The one big idea behind attention (the "I left my phone on the bank" example) • Encoder vs decoder - and why GPT and Claude use only the decoder • How multi-head attention splits 768 dimensions into 12 heads • Query, Key and Value explained with a networking + Google search analogy • What the feed forward layer, residual connections and layer norm really do • How the output head turns a vector into the next token (logits + softmax) • What causal masking, the generation loop, KV cache and TTFT mean This is Part 06 of the GenAI Fundamentals series - for data engineers, developers, and anyone learning how AI language models actually work. Watch the tokenization + vector embeddings video first if you haven't already. ===== Chapters ===== 00:00 What is a Transformer? (Attention Is All You Need) 02:07 Recap - Tokens, Embeddings and Dimensions 03:06 Why Transformers are Math Machines (Matrix Multiplication) 04:37 The One Big Idea Behind Attention 07:30 Encoder vs Decoder - What's the Difference? 10:57 Why GPT and Claude Use Only the Decoder 12:40 The 3 Families of Models (BERT, GPT, Transformer) 13:25 The Big Picture - Embedding, Blocks, Output Head 16:12 Inside a Single Transformer Block 18:53 What is Layer Normalization? 20:11 How Attention Works? 23:21 What is Multi-Head Attention? 26:02 Query, Key and Value Explained 28:44 The Attention Math - Scores and Softmax 34:30 What is the Feed Forward Layer? 38:36 The Output Head - From Vector to Next Token 39:04 What is Causal Masking? 43:36 The Generation Loop 44:13 What is KV Cache and TTFT? 45:55 Reading the "Attention Is All You Need" Diagram 48:00 Recap and What's Next (Prompt Engineering) Tokenization and Word Embedding Video - https://youtu.be/JyaAmvsel9w ===== Other Playlists ===== Checkout all other playlists on Data Engineering 👇🏻 https://www.youtube.com/@easewithdata/playlists ===== GitHub Repo ===== https://github.com/subhamkharwal https://github.com/subhamkharwal/genai-for-data-engineers ===== Connect with ME ===== LinkedIn - https://www.linkedin.com/in/subhamkharwal Medium - https://subhamkharwal.medium.com ===== References ===== Jay Alammar - https://jalammar.github.io/illustrated-transformer/ 3Blue1Brown - https://www.3blue1brown.com/lessons/attention/ ===== Hashtags ===== #Transformers #AttentionIsAllYouNeed #LLM #GenerativeAI #genai #dataengineering #neuralnetworks #machinelearning
04 How Large Language Models (LLMs) Works? | All about LLMs | What are Tokens & Context Length?
Generative AI | LLM | GenAI | NN | Large Language Models ⏰ Scheduled to be Public from Members Only on 01st Jun 2026 16:00 HRS IST ⏰ ===== In this video, you will learn ===== What is Large Language Model? What is LLM? How LLMs work? Next Token Prediction in LLM, What are Tokens and Context Length? Importance on Tokens in LLM, Different Sampling Controls, LLM Personas and Prompts, Probability Distribution for LLMs ===== Chapters ===== 00:00 - Introduction 00:27 - What are Large Language Models or LLMs? 03:44 - How Large is Large in LLMs? 05:44 - Transformers 08:07 - What are Tokens and their Importance in LLM? 08:18 - What is Vocabulary in LLM? 14:27 - Probability Distribution for Tokens 19:01 - Sampling Controls - Temperature, Top-p 25:51 - Auto-Regressive Generation Loop 28:53 - How LLMs preserves meaning? 30:58 - How LLMs are Trained? 33:15 - What is Fine Tuning? 34:28 - LLM Personas/Roles and Prompts 36:55 - What is Context Length? 39:01 - Model Knowledge Cutoff and Hallucination 41:23 - Open and Closed LLM Models 42:36 - Reasoning Models 43:24 - Multimodal Models ===== Links ===== Google's "Attention is all You Need" Paper - https://arxiv.org/pdf/1706.03762 Groq Cloud - https://console.groq.com/home GPT Tokenizer - https://platform.openai.com/tokenizer ===== Other Playlists ===== Checkout all other playlists on Data Engineering 👇🏻 https://www.youtube.com/@easewithdata/playlists ===== GitHub Repo ===== https://github.com/subhamkharwal ===== Connect with ME ===== LinkedIn - https://www.linkedin.com/in/subhamkharwal Medium - https://subhamkharwal.medium.com ===== Hashtags ==== #genai #dataengineering #python #agenticai #aiagents #aiagent #nn #neuralnetworks