Videos de How LLMs Work?
Videos etiquetados con "How LLMs Work?"
How LLMs Work? 7 videos
Large Language Models (LLMs) Explained From Scratch (Complete Beginner's Guide) | TAB 47
#largelanguagemodels #llmfullcourse #llmfromscratch Want to understand Large Language Models (LLMs) without complicated math? In this video, you'll learn what a Large Language Model is, how LLMs work, and why models like ChatGPT, Claude, Gemini, and DeepSeek are transforming AI. This complete LLM tutorial for beginners explains every fundamental concept step by step, including language models, AI models, next-word prediction, pre-training, fine-tuning, parameters, weights, biases, datasets, compute, and the difference between Small Language Models (SLMs) and Large Language Models (LLMs). Whether you're preparing for AI Engineering, Machine Learning, Generative AI, Prompt Engineering, or simply want to understand how modern AI works, this video builds a strong foundation from scratch. Click to start your Career in GenAI - MICROSOFT GenAI Course - https://bit.ly/4oxRGaF only Rs 299/ Join our WhatsApp Channel to get the latest updates, learning resources, job trends, and exclusive content : https://whatsapp.com/channel/0029Vb7v6JA3LdQdLhL9rQ2i Below are the concepts covered in this video : 00:26 – Introduction & What You'll Learn 01:54 – What Is a Large Language Model (LLM)? 03:50 – What Is Language? (Vocabulary, Grammar & Meaning) 05:31 – Programming Languages vs Human Languages 06:45 – What Is an AI Model? 10:33 – Models as Mathematical Approximations 12:13 – What Is a Language Model? 12:48 – Next Word Prediction Explained 15:10 – Why Is It Called a Large Language Model? 17:27 – Datasets, Parameters & Compute 18:43 – Weights, Biases & Parameters 19:22 – Small Language Models (SLMs) vs Large Language Models (LLMs) 21:48 – LLM Recap: Language + Model + Large 22:24 – Sequence Prediction Example 26:44 – Pre-training vs Fine-tuning 28:57 – Discriminative vs Generative Models 29:41 – What Is a Generative Model? #generativeai #deeplearning #llm #softwareengineer #webdevelopment #localllm #aiagents #aitools #llmtutorial
Large Language Models (LLMs) Explained | No Coding Required | Vishwa sir | Tab 47
#llm #machinelearning #softwareengineering Want to understand how ChatGPT, Claude, Gemini, and other modern AI models actually work? Join this LIVE LLM Masterclass where we'll break down Large Language Models (LLMs) in the simplest possible way—no coding or programming experience required. Whether you're a student, working professional, AI enthusiast, or someone planning to build AI applications in the future, this live session will help you understand the core concepts behind today's most powerful AI systems. 🔗 Ready to build a career in AI? Join upGrad's Artificial Intelligence Courses and start learning from industry experts: https://bit.ly/4uRBTFU What you'll learn: What are Large Language Models (LLMs)? How ChatGPT and modern AI models work Tokens, embeddings, transformers, and attention explained Training vs inference Prompt engineering fundamentals Open-source vs closed-source LLMs Real-world applications of LLMs How LLMs power AI Agents, RAG, copilots, and automation The best roadmap to start learning Generative AI
What Is a Large Language Model? (LLMs Explained From Zero)
Try it yourself — the full written explainer and an interactive next-word predictor are here: https://unrote.com/ai/what-is-an-llm/ A large language model sounds mysterious, but underneath it's one simple idea: a giant math function that, given some text, predicts the most likely next chunk of text — then feeds its own guess back in and does it again. That loop, repeated, is the whole engine. This is a build-from-zero explainer. No jargon assumed. We start with the autocomplete on your phone, watch a model generate a sentence one word at a time with real probabilities, and end up understanding why it can sound brilliant and still be confidently wrong. What we cover: - Why an LLM is really just autocomplete, scaled up enormously - Predict, append, repeat — how it writes one token at a time - Why it works in tokens (chunks), not whole words - Where the skill comes from: training on a huge pile of text - Why "Large" matters — billions of parameters - Why it doesn't actually "know" anything, and why that causes hallucinations - Why doing one simple thing well, at scale, ends up looking like intelligence Chapters: 0:00 What a large language model is 0:26 The whole thing in one sentence 0:44 Read the name backwards: L, L, M 1:03 It's autocomplete you already use 1:42 Watch it predict, word by word 2:36 It works in tokens, not words 2:59 Where the skill comes from: training 3:45 Why "Large" matters 4:10 It doesn't know — it predicts 4:33 So why does it feel intelligent? 4:58 Where this goes next 5:21 Recap This is the first video in an AI series on Unrote — modern AI explained from zero, one idea per page. Up next: What Are Tokens? Unrote. Understand it, don't memorize it.
✅ 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
LLMs Explained in 20 Minutes | The Transformer Behind ChatGPT, Gemini & Claude
🚀 LLMs Explained in 10 Minutes | The Transformer Behind ChatGPT, Gemini & Claude Ever wondered how ChatGPT, Gemini, Claude, and other AI assistants actually work? In this video, we'll break down Large Language Models (LLMs) in the simplest way possible. You'll learn how AI evolved from traditional neural networks to the revolutionary Transformer Architecture, the breakthrough that powers modern AI. We'll also explore the Attention Mechanism, the core idea that allows LLMs to understand context, focus on important words, and generate human-like responses. What You'll Learn ✅ What is an LLM (Large Language Model)? ✅ Why RNNs struggled with long context ✅ How Transformer Architecture works ✅ What is the Attention Mechanism? ✅ Why Transformers changed AI forever ✅ How ChatGPT, Gemini, and Claude generate responses ✅ The foundation behind modern Generative AI Whether you're a student, developer, cloud engineer, AI enthusiast, or preparing for AI interviews, this video will help you understand the fundamentals of LLMs without complicated math. 🔥 If you enjoy AI, Generative AI, Agentic AI, Google Cloud, Gemini, MCP, and modern AI architectures, make sure to subscribe for more content. #LLM #ChatGPT #Gemini #Claude #Transformer #GenerativeAI #ArtificialIntelligence #MachineLearning #AIExplained #TechTrapture Playlists Google Agent Development Kit (ADK) https://www.youtube.com/playlist?list=PLLrA_pU9-Gz2HwepRUVpq1TEPuYWo_fSi Learn Airflow https://www.youtube.com/playlist?list=PLLrA_pU9-Gz3i8qw6yakrfJzx75W_vVaH Learn Google Cloud in 2025 https://youtube.com/playlist?list=PLLrA_pU9-Gz2OnBoICkewd9-Fc9Mi0nm7&si=8kkB3ct5wDHCMkoi Data Engineering Hands-on Projects https://www.youtube.com/playlist?list=PLLrA_pU9-Gz2DaQDcY5g9aYczmipBQ_Ek Looking to get in touch? Drop me a line at vishal.bulbule@techtrapture.com Linkedin https://www.linkedin.com/in/vishal-bulbule/ Medium Blog https://medium.com/@VishalBulbule Github Source Code https://github.com/vishal-bulbule
How LLMs Actually Generate Text (Every Dev Needs to See This)
Every day, millions of people use ChatGPT, Claude, and Grok—but very few understand what is actually happening behind the blinking cursor. Did you know the model has no idea what it's going to say next? In this video, we break down the exact 5-step process of how Large Language Models (LLMs) generate text, from the moment you hit "send" to the final output. We move past the magic and dive into the mechanism so you can become a better AI builder. You’ll learn exactly how AI reads text, how it understands context, and why "hallucinations" actually happen. 👇 What You Will Learn (Chapters): 0:00 - The Illusion of AI (No Hidden Script) 0:28 - The 5 Steps of LLM Text Generation 0:54 - Step 1: Tokenization (How Models Read) 1:56 - Step 2: Embeddings (Mapping Meaning & Context) 3:11 - Step 3: Transformers & The Attention Mechanism 4:40 - Step 4: Probabilities (Logits & Softmax) 5:40 - Step 5: Sampling (Greedy Decoding, Temperature & Top-P) 6:52 - Autoregressive Generation (The Loop) 7:50 - Why AI Hallucinates (Mechanism, Not Magic) 8:50 - Summary: Becoming an AI Builder If you want to understand the architecture of modern AI, hit the LIKE button and SUBSCRIBE for more deep dives into machine learning and software engineering. #ChatGPT #LLM #MachineLearning #ArtificialIntelligence #Transformers #OpenAI #TechEducation #LLM #HowAIWorks #ChatGPT #MachineLearning #ArtificialIntelligence #NeuralNetworks #Transformers #TechExplained #Programming #OpenAI #Claude 🏷️Keywords ChatGPT, OpenAI, Large Language Models, LLM, Claude, Grok, Artificial Intelligence, AI explained, Machine Learning, Neural Networks, Deep Learning, Generative AI, generative text, Specific & Technical Tags: Tokenization, AI tokens explained, Word embeddings, Transformer model explained, Attention mechanism AI, Self attention, Softmax function, AI logits, Top-p sampling, Nucleus sampling, AI Temperature setting, Greedy decoding, Autoregressive models, Llama 3, GPT-4, Long-Tail/Search Query Tags: How does ChatGPT work, How large language models work, What is a token in AI, Transformer neural network explained simply, How AI generates text, Why does AI hallucinate, ChatGPT temperature explained, How to write better AI prompts, Mechanism not magic, Learn AI for beginners, How to build with LLMs, AI context window explained,
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