Videos de llm
Videos etiquetados con "llm"
llm 22 videos
Google Gemini 3.5 Pro Explained | 2M Tokens, Deep Think & GPT-6 Comparison
Link to our newsletter: https://bitbiased.ai/ Google's Gemini 3.5 Pro is rumored to launch with a massive 2 million token context window, a new Deep Think reasoning mode, and significantly cheaper API pricing. But are these leaks actually as groundbreaking as everyone claims? In this video, we break down: ✅ Gemini 3.5 Pro leaks explained ✅ 2 Million Token Context Window ✅ Deep Think reasoning mode ✅ API pricing and why it matters ✅ Gemini 3.5 Pro vs GPT-6 ✅ Gemini 3.5 Pro vs Claude Fable 5.1 ✅ Google AI strategy in 2026 ✅ What actually deserves the hype Instead of repeating AI rumors, this video separates confirmed information from speculation and explains what Google's strategy could mean for developers, businesses, and AI enthusiasts. If you're following GPT-6, OpenAI, Google DeepMind, Anthropic, Claude, or the future of generative AI, this video is for you. Timestamps 00:00 Introduction 01:23 What's Actually Being Claimed 02:48 The Two Million Tokens Everyone's Chasing 04:54 The Price Is The Real Story 06:55 Deep Think & The Paywall 08:37 Gemini 3.5 Pro vs GPT-6 & Claude Fable 5.1 10:43 What Would Actually Earn The Hype 12:21 Final Thoughts Subscribe for weekly AI news, model comparisons, benchmark analysis, and practical insights. #Gemini35Pro #googleai #gpt6 #openai #artificialintelligence #deepthinking #ai #claude #googledeepmind #machinelearning
GRPO Fine-Tuning with Practical | DeepSeekMath, PPO vs GRPO, Hugging Face & Unsloth
Learn GRPO (Group Relative Policy Optimization) from scratch and fine-tune an LLM using Hugging Face TRL and Unsloth. In this video, we understand how GRPO works, why it was used in DeepSeekMath, how it differs from PPO, and how language models learn from reward signals. We will also implement GRPO fine-tuning practically using Hugging Face and Unsloth. Topics covered in this video: ✅ What is GRPO? ✅ GRPO full form and intuition ✅ Quick background of DeepSeekMath ✅ DeepSeekMath training pipeline ✅ PPO vs GRPO ✅ Problems with the PPO approach ✅ Why GRPO does not require a critic/value model ✅ Group-based reward comparison in GRPO ✅ GRPO step-by-step with a simple example ✅ Reward signal vs reward model ✅ Rule-based and verifiable rewards ✅ Correctness, helpfulness and clarity reward functions ✅ LoRA-based GRPO fine-tuning ✅ GRPO practical using Hugging Face TRL ✅ GRPO practical using Unsloth ✅ Loading and testing the fine-tuned model GRPO stands for Group Relative Policy Optimization. Instead of using a separate value or critic model, GRPO generates multiple answers for the same prompt, compares their rewards within the group and improves the policy using relative performance. This video is useful for machine learning engineers, Generative AI developers, data scientists and anyone learning LLM fine-tuning, reinforcement learning, RLHF, PPO, GRPO, DeepSeek and post-training techniques. Subscribe for more videos on Generative AI, LLM fine-tuning, RAG, Agentic AI, RLHF, PPO and GRPO. Code: https://github.com/sunnysavita10/Complete-LLM-Finetuning/tree/main/LLM%20Fine-Tuning-27-GRPO #GRPO #LLMFineTuning #DeepSeek #HuggingFace #Unsloth 📌 Subscribe for more videos on: LLM Fine-Tuning, RLHF, Quantization, Hugging Face, LangChain, Agentic AI, RAG, AI Systems, and Production-Grade AI Projects. #RLHF #PreferenceAlignment #LLM #PPO #ReinforcementLearning #DPO #ORPO #Qlearning #DQN #LLMFineTuning #GenerativeAI #MachineLearning #SunnySavita #AgenticAI #LangChain #ArtificialIntelligence 📌 Keywords Covered: #MultimodalLLM #VisionLanguageModel #MultimodalFineTuning #LLMFineTuning #Unsloth #LLaVA #QwenVL #Pixtral #LlamaVision #LoRA #QLoRA #VisionEncoder #ProjectionLayer #HuggingFace #Transformers #GenerativeAI #AIForDevelopers #CustomDataset #ImageToText #AITraining #SunnySavita #SemanticSearch #RAG Multimodel RAG Playlist: https://www.youtube.com/watch?v=7CXJWnHI05w&list=PLQxDHpeGU14D6dm0rmAXhdLeLYlX2zk7p&pp=gAQBiAQB RAG detailed playlist: https://www.youtube.com/watch?v=wTVTkOb3SZc&list=PLQxDHpeGU14Blorx3Ps1eZJ4XvKET1_vx&pp=gAQBiAQB GenAI Foundation Playlist: https://www.youtube.com/watch?v=ajWheP8ZD70&list=PLQxDHpeGU14D7NiPgqxC9qhKkx4jMQcDk&pp=gAQBiAQB Connect with me on social media LinkedIn: https://www.linkedin.com/in/sunny-savita/ One-to-One Call: https://topmate.io/sunny_savita10 GitHub: https://github.com/sunnysavita10
😎 Claude 입문 강의! ChatGPT만 쓰던 사람이라면 꼭 보세요 #소이랩왕초 #claude #antropic
😎 [ 왕초 ] 와 함께하는 '나도 잘 할꾸야' Claude~ 요즘 정말 많이 들리는 AI, Claude! 도대체 ChatGPT랑 뭐가 다르고, 왜 개발자들이 많이 사용할까요? + Claude는 뭔가요? + ChatGPT와 뭐가 다른가요? + 실제로 무엇을 할 수 있나요? + 비용은 얼마나 드나요? 🚀소이랩 7번째 신작! 온라인 강의 (오픈) '프로에게 배우는 CLOUD COMFY' : https://fastcampus.co.kr/data_online_cloud 🚀소이랩 6번째 온라인 강의 (완결) '프로에게 배우는 나노바나나!' : https://fastcampus.co.kr/data_online_kontext 💬카카오톡 실시간 채팅 채널 - 소이랩 [생생정보통] : https://open.kakao.com/o/gs60FZgh 📢콘텐츠 개발 및 교육문의 : contact@soylab.ai - 00:13 Intro 00:31 클로드가 뭔가요? 06:36 클로드의 현재? 09:09 뭘 할 수 있나요? 10:51 가격 14:43 Outro - 콘텐츠 개발 및 교육문의 : contact@soylab.ai 카카오톡 실시간 채팅 채널 - 소이랩 [생생정보통] : https://open.kakao.com/o/gs60FZgh #claude #anthropic #chatgpt #gpt #ai #생성형AI #AI입문 #왕초보 #AI기초 #MCP #클로드 #클로드AI #AI에이전트 #바이브코딩 #VibeCoding #cursor #codex #openai #개발AI #AI자동화 #stablediffusion #comfyui #소이랩 #soylab
Fundamentos conceptuales de la IA, ¿Qué es un LLM?, ¿Qué puede y qué NO puede garantizar la IA?
Que es un LLM y como funciona realmente la inteligencia artificial que usamos todos los dias? En este video te explicamos, desde cero y sin tecnicismos, los fundamentos de como funciona un modelo de lenguaje como Claude, ChatGPT o Gemini: que puede hacer, que no puede garantizar, y como diferenciarlos entre si. Vas a aprender: - Que es un LLM y por que "predice" texto en lugar de "saber" hechos - Que es un token y una ventana de contexto, explicado con analogias simples - Quien es Anthropic y que hace diferente a Claude de otros asistentes - Que hace bien la IA y que NO deberias confiarle sin verificar - Por que ocurren las alucinaciones y como detectarlas - Vocabulario esencial de IA explicado en simple: prompt, token, fine-tuning y mas Ideal para docentes, estudiantes y cualquier persona que quiera entender la inteligencia artificial de forma critica y responsable, antes de empezar a usarla. Si te sirvio, dale LIKE, SEGUIME y COMPARTI este video para que le llegue a mas personas. #InteligenciaArtificial #IA #ChatGPT #Claude #Educacion #LLM #TecnologiaEducativa #AlfabetizacionDigital
Google Eliminó Gemini CLI... ¿Quién Será el Siguiente? La Verdad Detrás del Caos
¿Google está perdiendo la carrera de la inteligencia artificial o simplemente está perdiendo la confianza de los desarrolladores? En este video analizamos el polémico fin de Gemini CLI, la llegada de Antigravity, los cambios en los límites de uso de Gemini, la inversión récord de Google en IA y por qué miles de programadores reaccionaron con frustración durante Google I/O 2026. Descubre qué ocurrió realmente, por qué tantos usuarios están migrando a otras herramientas de IA y qué significa todo esto para el futuro del desarrollo de software con inteligencia artificial. Si te interesan temas como Google AI, Gemini, Gemini CLI, Antigravity, Google I/O, Claude Code, OpenAI, Cursor AI, GitHub Copilot, programación con IA, agentes de IA, desarrollo de software, LLMs, IA generativa y las últimas noticias sobre inteligencia artificial, este video es para ti. ⏱️ Capítulos 00:00 ¿Qué pasó con Google? 00:48 La mejor semana de Google... y su mayor polémica 02:02 El cambio de límites de Gemini que enfureció a los usuarios 03:15 Google elimina Gemini CLI y presenta Antigravity 04:32 El editor que desapareció de la noche a la mañana 05:45 ¿Por qué Google reaccionó tan rápido? 06:37 La nueva realidad del negocio de la IA 07:28 ¿Google realmente está perdiendo la carrera de la IA? 08:30 El verdadero problema: la confianza de los desarrolladores 09:30 ¿Qué viene ahora para Google AI y Gemini? En este canal analizamos cada semana las noticias más importantes sobre Inteligencia Artificial, ChatGPT, OpenAI, Google Gemini, Claude, Anthropic, DeepSeek, Meta AI, LLMs, agentes de IA, programación asistida por IA y las tecnologías que están transformando el desarrollo de software. 💬 ¿Crees que Google cometió un error al eliminar Gemini CLI? ¿Confiarías tu trabajo diario a una plataforma que cambia constantemente? Déjalo en los comentarios. 👍 Si te gustó este análisis, suscríbete y activa la campana para no perderte los próximos videos sobre inteligencia artificial, programación y las últimas novedades del mundo de la IA. #GoogleAI #Gemini #GeminiCLI #Antigravity #GoogleIO #InteligenciaArtificial #IA #Programacion #DesarrolloDeSoftware #Claude #OpenAI #CursorAI #GitHubCopilot #LLM #AIAgents
Spring AI 2.0: Custom Advisors for Tool & Token Logging
Ever wondered which tools your LLM is actually using and how many tokens each call is consuming? Spring AI Advisors give you AOP-like superpowers for your AI interactions, letting you intercept, log, and monitor every call to your language model. In this video, we explore Spring AI's Advisor API by building two custom advisors from scratch. The first is an AvailableToolsLoggingAdvisor that shows which tools are visible to the model before a call and which ones were actually invoked after. The second is a TokenCounterAdvisor that tracks prompt tokens, completion tokens, and total tokens with running totals across multiple calls. Along the way, you'll learn how advisors work as before/after interceptors for LLM calls, how to use the ChatResponse metadata, and how to wire tools into your Spring AI chat client. - Understand what Spring AI Advisors are and how they act as AOP for LLM calls - Build an AvailableToolsLoggingAdvisor to log which tools are loaded and which are invoked - Build a TokenCounterAdvisor to track prompt, completion, and total token usage per call - Learn how to use ChatResponse metadata for usage statistics - Wire up custom tools (like a DateTime tool) and see them in action with advisors 0:00 - Intro - Why I needed custom advisors 1:10 - What are Spring AI Advisors? 2:30 - Spring AI Reference Docs & Built-in Advisors 3:30 - Project setup on start.spring.io 4:30 - Configuring API key and model 5:15 - Creating the ChatController and ChatResponse 6:45 - Understanding token usage from ChatResponse 8:00 - Adding a DateTime tool for LLM calls 10:00 - Building the AvailableToolsLoggingAdvisor (before) 13:30 - Adding the after method to log invoked tools 16:30 - Testing the AvailableToolsLoggingAdvisor 17:45 - Building the TokenCounterAdvisor 20:30 - Testing the TokenCounterAdvisor 21:30 - Wrap up & what's next (Tool Searching) 🔗Resources & Links mentioned in this video: Spring AI Reference Documentation - Advisors: https://docs.spring.io/spring-ai/reference/api/advisors.html Spring Initializr: https://start.spring.io Dan Vega's Spring AI Workshop (GitHub): https://github.com/danvega/spring-ai-workshop 👋🏻Connect with me: Website: https://www.danvega.dev Twitter: https://twitter.com/therealdanvega Github: https://github.com/danvega LinkedIn: https://www.linkedin.com/in/danvega Newsletter: https://www.danvega.dev/newsletter SUBSCRIBE TO MY CHANNEL: http://bit.ly/2re4GH0 ❤️
As IAs generativas são máquinas de adivinhar?
Você já se perguntou por que ferramentas como ChatGPT, Gemini e Copilot conseguem produzir respostas tão convincentes? Será que elas realmente sabem ou apenas adivinham? Neste vídeo, explicamos de forma simples e objetiva como funcionam as IAs generativas por trás dos grandes modelos de linguagem (LLMs). Você vai entender por que esses sistemas são frequentemente descritos como máquinas de previsão, o que são os famosos tokens e por que uma IA pode gerar respostas impressionantes... e, ao mesmo tempo, cometer erros surpreendentes! Se você trabalha com tecnologia, dados, inteligência artificial ou simplesmente quer entender o que acontece por trás dessas ferramentas, este vídeo é para você. 📚 Fonte principal • Artigo da University of Central Florida: https://www.ucf.edu/artificial-intelligence/how-do-generative-ai-tools-like-chatgpt-work/ Capítulos: 00:00 - As IAs generativas parecem inteligentes... Não parecem? 00:32 - O artigo publicado pela University of Central Florida. 01:27 - O que são "tokens"? 01:57 - O comentário do pato. 02:13 - Por que a IA generativa erra? 02:52 - Encerramento.
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
How AI Steals? Borrow? Creates? Images From Text
Ever wondered how typed words instantly turn into stunning AI images? 🤖✨ In this video, we break down the mind-blowing science behind text-to-image AI (like Midjourney, DALL-E, and Stable Diffusion) in simple terms anyone can understand! From decoding your text prompts to understanding how "Diffusion Models" actually paint pixels out of random static noise, we pull back the curtain on how artificial intelligence "thinks" in pictures. No coding experience required! If this video helped you understand AI a bit better, smash that LIKE button and SUBSCRIBE for more simple tech breakdowns! 🔔What’s the craziest prompt you’ve ever given an AI? Drop it in the comments below! 👇
LLM 101: From Basics to Advanced in Minutes
LLM 101: From Basics to Advanced in Minutes What is an LLM? How do Large Language Models work? How does ChatGPT generate human-like responses? In this video, we break down Large Language Models (LLMs) from basics to advanced concepts in a simple and practical way. If you’ve heard terms like LLM, Large Language Models, ChatGPT, Generative AI, Transformers, Tokens, Embeddings, RAG, Fine-Tuning, Prompt Engineering, and AI Agents, this video will help you understand how they all fit together. Large Language Models are the foundation of modern Artificial Intelligence and power tools such as ChatGPT, Claude, Gemini, Copilot, Perplexity, and many enterprise AI applications. What You’ll Learn ✅ What is a Large Language Model (LLM)? ✅ How Large Language Models Work ✅ How ChatGPT Works ✅ What Are Tokens in LLMs? ✅ Transformer Architecture Explained ✅ Neural Networks Explained ✅ How LLMs Are Trained ✅ What Are Embeddings? ✅ Fine-Tuning vs Prompt Engineering ✅ What is Retrieval-Augmented Generation (RAG)? ✅ AI Agents Explained ✅ Real-World Applications of Large Language Models Large Language Models Explained A Large Language Model (LLM) is an Artificial Intelligence model trained on massive amounts of text data to understand and generate human language. Large Language Models use Machine Learning, Deep Learning, Transformer Architecture, and Neural Networks to predict the next word in a sequence and generate intelligent responses. Modern LLMs power: * ChatGPT * Claude * Gemini * Microsoft Copilot * Enterprise AI Assistants * AI Agents * Customer Support Bots * AI Search Engines * Business Automation Systems Key Concepts Covered * Large Language Models (LLMs) * Artificial Intelligence * Machine Learning * Deep Learning * Transformer Models * Neural Networks * Tokens * Embeddings * Context Windows * Prompt Engineering * Fine-Tuning * RAG Architecture * AI Agents * Generative AI * Natural Language Processing (NLP) Real-World Applications of LLMs Learn how Large Language Models are transforming: ✔ Business Operations ✔ Supply Chain Management ✔ Customer Support ✔ Content Creation ✔ Research and Analysis ✔ Marketing and Sales ✔ Software Development ✔ Knowledge Management ✔ Enterprise Productivity Who Should Watch? * AI Beginners * Working Professionals * Managers * Business Leaders * Students * Data Analysts * Supply Chain Professionals * Operations Managers * Entrepreneurs * Technology Enthusiasts
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
Day - 03 : GEN AI + LLM + RAG + Agentic AI Overview by Mr. Ashok
Artificial Intelligence is changing the way software applications are built. In this session, Mr. Ashok explains the most important AI concepts every student and developer should know. In this video, you will learn: ✅ What is Generative AI? ✅ What are Large Language Models? ✅ How RAG works in real-time applications ✅ What is Agentic AI? ✅ Difference between LLM, RAG, and AI Agents ✅ Real-world use cases of Gen AI ✅ Career opportunities in AI This session is useful for students, freshers, working professionals, Java developers, Python developers, and anyone who wants to start learning AI from basics. 📌 Watch the full session and start your AI learning journey today. #GenAI #LLM #RAG #AgenticAI #ArtificialIntelligence #AIOverview #GenerativeAI #PromptEngineering #AIForBeginners #AshokIT #MrAshok #PythonAI #AICareer #MachineLearning #TechLearning