Videos de deeplearning
Videos etiquetados con "deeplearning"
deeplearning 7 videos
Write Your First AI LLM Call with LangChain & Groq
Here's a YouTube description optimized for beginners, searchability, and engagement: 🚀 Welcome to LangChain for Beginners! In this first video of the series, you'll learn how to make your very first LLM call using LangChain and the Groq API. We'll start with the fundamentals of chat models, understand how messages work, create a ChatGroq model, send prompts to an LLM, and inspect the response returned by the model. By the end of this tutorial, you'll understand the core interaction pattern used throughout LangChain: Messages → Model → Response 📚 What You'll Learn: ✅ What a chat model is ✅ SystemMessage vs HumanMessage ✅ How to connect LangChain to Groq ✅ Creating your first ChatGroq model ✅ Using invoke() to call an LLM ✅ Understanding AIMessage responses ✅ Exploring response metadata and token usage 🔗 Code Covered: • Loading environment variables with python-dotenv • Creating a ChatGroq model • Building conversations with messages • Making your first LLM request • Reading model responses This video is part of the LangChain for Beginners series, where we'll gradually build toward prompt templates, chains, LCEL, embeddings, RAG applications, tools, and AI agents. ⏱️ Chapters 00:00 Introduction 00:45 How Chat Models Work 02:10 Loading Environment Variables 03:15 Creating a ChatGroq Model 05:20 Understanding Messages 07:40 Invoking the Model 09:15 Reading Responses 10:30 Response Metadata Explained 12:00 Recap 💡 If you found this video helpful, consider liking the video and subscribing for more AI Engineering, LangChain, RAG, Agentic AI, and LLM tutorials. #LangChain #LLM #AIEngineering #GenerativeAI #Python #Groq #AIAgents #MachineLearning #ArtificialIntelligence #RAG #PromptEngineering
How AI Creates Art From Chaos
How AI Creates Art From Chaos — type a few words, and seconds later a stunning, original image appears. But here's the wild part: it didn't start from a blank canvas. It started from pure noise — random static — and slowly carved order out of chaos. This is how AI image generation actually works. The diffusion models behind tools like Stable Diffusion, DALL·E, and Midjourney don't "paint" the way we imagine. They learn to reverse chaos itself. In this video, we break down the beautiful, counterintuitive process step by step. What we cover: 🌫️ Starting from noise — why AI image generation literally begins with random static, not a blank page ➕ The forward process — how models learn by adding noise to real images until they're unrecognizable ➖ The reverse process — the real magic: learning to remove noise, step by step, to reveal an image 🧭 Text guidance — how your prompt steers the denoising toward what you asked for 🗜️ Latent diffusion — doing it all in a compressed space to make it fast and efficient 🎨 Why it works so well — what makes diffusion the dominant approach in generative vision ⚠️ The bigger picture — creativity, copyright, and the ethics of AI-made art Whether you're an artist, a builder, or just amazed by what these tools can do, this is the clear story of how machines turn chaos into creativity. 🔔 Subscribe to NeuraForge for more on generative AI, deep learning, NLP, and computer vision. 💬 What's the most impressive AI-generated image you've seen? Drop it (or describe it) in the comments. #DiffusionModels #AIArt #GenerativeAI #StableDiffusion #TextToImage #DeepLearning #MachineLearning #ComputerVision #NeuraForge #AIExplained
¡Redes Neuronales que Explican sus Decisiones! El Diccionario Interpretable en Sparse Coding
Las redes neuronales artificiales, especialmente los modelos de aprendizaje profundo, a menudo son consideradas "cajas negras". Esto se debe a que su funcionamiento interno y la forma en que representan los datos son increíblemente complejos y difíciles de interpretar para los humanos. No podemos entender fácilmente por qué toman una decisión específica, lo cual es un problema fundamental cuando queremos confiar en sistemas de inteligencia artificial para tareas críticas. Este estudio presenta un enfoque novedoso para entrenar una red neuronal de una manera que sus componentes internos sean comprensibles, inspirándose en los mecanismos de representación y aprendizaje del cerebro de los mamíferos. Utilizando una técnica llamada "codificación dispersa" (sparse coding), el modelo aprende un "diccionario" de elementos que son más fáciles de entender. A diferencia de los modelos tradicionales, este método obliga a la red a ser selectiva, utilizando solo unos pocos elementos para explicar una entrada de datos, de forma similar a como nuestro cerebro procesa la información de manera eficiente. Los resultados demuestran que el modelo de codificación dispersa ofrece beneficios tanto cualitativos como cuantitativos en la interpretación en comparación con modelos equivalentes como los autoencoders convolucionales. Las representaciones internas aprendidas son mucho más claras y selectivas, lo que permite a los investigadores entender la contribución de cada neurona a la decisión final del sistema. Este trabajo es un paso adelante hacia la creación de una inteligencia artificial más transparente y fiable. Link al paper: https://arxiv.org/pdf/2011.11805 Autores del estudio: Edward Kim, Connor Onweller, Andrew O'Brien, Kathleen McCoy Apoyanos en https://www.patreon.com/audioarxiv Unete en https://discord.gg/vKRmFhg4YQ #Ciencias de la computación #InteligenciaArtificial #MachineLearning #RedesNeuronales #DeepLearning #IAExplicable
Alinear y Memorizar: La Clave del Aprendizaje en Redes Neuronales Profundas
Este estudio explora una alternativa eficiente y biológicamente plausible a la retropropagación para entrenar redes neuronales profundas, conocida como Alineación de Retroalimentación Directa (DFA). A pesar de su éxito en modelos como los Transformers, la DFA falla notablemente en redes convolucionales, y este trabajo busca desentrañar el porqué de esta discrepancia. La investigación propone una nueva teoría que describe el aprendizaje con algoritmos de alineación de retroalimentación. Se demuestra que el aprendizaje se desarrolla en dos fases distintas: una fase inicial de 'alineación', donde el modelo ajusta sus pesos para alinear el gradiente aproximado con el gradiente real de la función de pérdida, seguida de una fase de 'memorización', en la que el modelo se centra en ajustar los datos para minimizar el error. Este proceso de 'alinear y luego memorizar' no solo explica por qué la DFA converge naturalmente a soluciones que maximizan la alineación del gradiente, sino que también ofrece una explicación para su fallo en las redes neuronales convolucionales. Los hallazgos, respaldados por experimentos numéricos, muestran cómo este mecanismo opera secuencialmente desde las capas inferiores hasta las superiores de la red, abriendo nuevas vías para entender y mejorar los algoritmos de aprendizaje profundo. Link al paper: https://arxiv.org/pdf/2011.12428 Autores del estudio: Maria Refinetti, Stéphane d'Ascoli, Ruben Ohana, Sebastian Goldt Apoyanos en https://www.patreon.com/audioarxiv Unete en https://discord.gg/vKRmFhg4YQ #Ciencia de la computación #InteligenciaArtificial #MachineLearning #RedesNeuronales #DeepLearning #FeedbackAlignment
Unlocking the Secrets: How Large Language Models Actually Work!
Unlocking the Secrets: How Large Language Models Actually Work! Join us for an enlightening webinar that dives deep into the mechanics of Large Language Models (LLMs)! 🌐 Whether you’re a tech enthusiast, a developer, or just curious about AI, this session is tailored for you. In this comprehensive webinar, we will demystify the complex architecture behind LLMs and provide insights into their functioning, applications, and implications for the future of technology. 📊 🔍 What to Expect: Understanding LLMs: Learn the foundational concepts behind Large Language Models, including their architecture and training processes. Real-World Applications: Explore how LLMs are transforming industries—from customer service to content creation and beyond! Ethical Considerations: Discuss the ethical implications of LLMs, including biases, misinformation, and the importance of responsible AI. Future Trends: Get a glimpse into the future of AI and language processing technologies. This interactive session includes a Q&A segment where you can ask questions and engage with our expert panel. Don’t miss this chance to enhance your understanding of one of the most exciting advancements in AI! 🎤 Register now and be part of the conversation that shapes the future of technology! 💼 For Any Business Requirement: https://www.sjinnovation.com/contact-us Facebook: https://www.facebook.com/sjinnovation Twitter: https://twitter.com/sjinnovation LinkedIn: https://www.linkedin.com/company/sj-innovation Instagram: https://www.instagram.com/sj_innovation/ Pinterest: https://in.pinterest.com/sjinnovationllc/ Facebook Bangladesh: https://www.facebook.com/sjinnovationbangladesh Facebook Goa: https://www.facebook.com/sjinnovationgoa Get top-notch healthcare software solutions from SJ Innovation 🏥 Streamline your practice, improve patient care, and boost efficiency with our cutting-edge software 💻 👉 Check out our services here: Drupal Development: https://sjinnovation.com/drupal-service Quality Assurance & UAT: https://sjinnovation.com/quality-assurance-uat Email & Landing Page Design: https://crafted.email Mobile App Development: https://sjinnovation.com/mobile-app-development AWS Cloud Services: https://sjinnovation.com/aws-cloud-services Magento Services: https://sjinnovation.com/magento-development-service Headless CMS Development: https://sjinnovation.com/headless-cms-service Flutter Services: https://sjinnovation.com/flutter-services MERN Service: https://sjinnovation.com/mern-service QA Automation: https://sjinnovation.com/qa-automation Shopify Service: https://sjinnovation.com/shopify-service #LargeLanguageModels #LLMs #AIWebinar #LanguageProcessing #MachineLearning #NaturalLanguageProcessing #AIApplications #TechSeminar #AITrends #EthicalAI #AIArchitecture #DeepLearning #DataScience #AICommunity #AIInsights #TechEducation #FutureOfAI #AIImplications #AIForBusiness #TechnologyInnovation #AIResearch #AIChallenges #LanguageModels #ModelTraining #ConversationalAI
AI vs Machine Learning vs Deep Learning Explained Simply 🤖 | Full Beginner Guide 2026
🚀 Want to understand the difference between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)? In this video, we explain everything in simple Indian English with real-world examples, interview concepts, career guidance, and industry use cases. Perfect for students, freshers, software engineers, and anyone starting their AI journey in 2026. 🔥 In This Video: ✔ What is Artificial Intelligence? ✔ What is Machine Learning? ✔ What is Deep Learning? ✔ AI vs ML vs DL Differences ✔ Real-world Applications ✔ AI Engineer Career Roadmap ✔ Skills Required in 2026 ✔ Salary & Job Opportunities ✔ Beginner-Friendly Explanation ✔ Interview Questions & Answers 💡 Whether you're preparing for placements, coding interviews, or starting a tech career, this video will give you a complete understanding of AI technologies shaping the future. 📌 Topics Covered: Artificial Intelligence Machine Learning Deep Learning Neural Networks Generative AI AI Careers Data Science AI Engineering ChatGPT & Modern AI Tools 🔥 Subscribe for more: AI Tutorials • Cloud Computing • Interview Preparation • Career Guidance • Tech Trends • Coding Roadmaps #AI,#MachineLearning,#DeepLearning,#ArtificialIntelligence,#AI2026,#MLEngineer,#AIEngineer,#DataScience,#ChatGPT,#GenerativeAI,#Python,#Coding,#Tech,#InterviewPreparation,#SoftwareEngineer,#NeuralNetworks,#CloudComputing,#FutureOfAI,#AITutorial,#MLTutorial,#DeepLearningTutorial,#Programming,#Students,#TechCareer,#AIJobs,#AIInterviewQuestions,#LearnAI,#CodingInterview,#AIForBeginners,#techeducation #AI,#MachineLearning,#DeepLearning,#ArtificialIntelligence,#AI2026,#ChatGPT,#OpenAI,#GenerativeAI,#AITools,#AIEngineer,#MachineLearningEngineer,#DeepLearningAI,#NeuralNetworks,#Python,#Coding,#Programmer,#SoftwareEngineer,#DataScience,#DataScientist,#BigData,#Tech,#Technology,#TechNews,#Innovation,#FutureTech,#FutureOfAI,#LearnAI,#AITutorial,#MLTutorial,#DeepLearningTutorial,#CodingTutorial,#Programming,#ComputerScience,#Developer,#FullStackDeveloper,#CloudComputing,#AWS,#Azure,#GoogleCloud,#DevOps,#CyberSecurity,#InterviewQuestions,#InterviewPreparation,#PlacementPreparation,#FreshersJobs,#EngineeringStudents,#CollegeStudents,#StudyMotivation,#CareerGrowth,#HighSalarySkills,#PassiveIncome,#OnlineLearning,#TechCareer,#AIJobs,#RemoteJobs,#Startup,#Business,#DigitalMarketing,#Productivity,#Automation,#AIApps,#ViralVideo,#Trending,#ExplorePage,#YouTubeGrowth,#YouTubeSEO,#ContentCreator,#Vlog,#HindiTech,#IndianYouTuber,#TechIndia,#LearnCoding,#CodeWithAI,#AITips,#SmartStudents,#Education,#SkillDevelopment,#FutureSkills,#NoCode,#100DaysOfCode,#Reels,#ViralReels,#TechReels,#Shorts,#YouTubeShorts,#TrendingNow,#ML,#DL,#LLM,#GPT4,#OpenAIChatGPT,#AICommunity,#ArtificialGeneralIntelligence,#AGI,#AIRevolution,#NextGenAI,#AIExplained,#TechExplained,#BTech,#EngineeringLife,#CampusPlacement,#JobReady,#SelfImprovement,#SuccessMindset,#MakeMoneyOnline,#StudentLife,#AIForStudents,#LearnMachineLearning,#DeepLearningProjects,#AIProjects,#PythonProjects,#CodingLife,#ProgrammersLife,#TechWorld,#DigitalFuture,#InternetOfThings,#Blockchain,#SaaS,#Entrepreneurship,#Freelancing,#RemoteWork,#TechChannel,#EducationChannel,#Knowledge,#Motivation,#CareerTips,#CloudEngineer,#DataEngineer,#SoftwareJobs,#StudyWithMe,#LearnWithMe,#ExamPreparation,#CodingMotivation,#InnovationTech,#AIContent,#AIUpdates,#TrendingTech,#FutureCareer
Inteligencia Artificial vs Machine Learning vs Deep Learning | Machine Learning 101
Entra a pyninja.pro y comienza a aprender Machine Learning hoy mismo. En este video, explicaremos a detalle tres términos: Inteligencia Artificial (IA), Machine Learning (ML) y Deep Learning (DL). Conoceremos sus características y diferencias que los distinguen. #inteligenciaartificial #ia #machinelearning #deeplearning Más videos: Diferencia entre aprendizaje supervisado y NO supervisado | Machine Learning 101 https://www.youtube.com/watch?v=d1tVOcZwM3s Evalúa un modelo de Machine Learning con validación cruzada | Machine Learning 101 https://www.youtube.com/watch?v=84uQXprM8aY&t=86s Diferencia entre regresión lineal simple y múltiple | Machine Learning 101 https://www.youtube.com/watch?v=HXahn-XGn2Q Temas del video: - Inteligencia artificial - Machine learning - Deep learning Sígueme en: Twitter: https://twitter.com/pyninja_ Contacto: pyninja.official@gmail.com Acerca de pyninja: pyninja es una plataforma (https://pyninja.io/) y un canal de YouTube (https://youtube.com/@pyninja) especializado en la producción de contenido educativo sobre Machine Learning con Python. Nos enfocamos en proporcionar recursos en español mediante video tutoriales y cursos para facilitar el aprendizaje. Capítulos: 00:00 - Intro 00:25 - Inteligencia Artificial (IA) 01:44 - Machine Learning (ML) 02:46 - Deep Learning (DL) 03:56 - Resumen 04:28 - Outro