Videos de machinelearning
Videos etiquetados con "machinelearning"
machinelearning 10 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
How Modern AI Systems Actually Work: RAG, Tool Calling & LLMs
In this MizuFlow.ai Foundation of Finance episode, Sung Lee, CFA, CPA, CA, explains the core architectural principles behind modern enterprise AI systems. Many organizations focus exclusively on the Large Language Model itself. However, real business value comes from combining: • LLM reasoning • enterprise data • tool calling • APIs • Retrieval Augmented Generation (RAG) • structured outputs • workflow orchestration This session explores how these components work together to create intelligent systems capable of solving real-world business problems. The objective is to help finance, accounting, and technology professionals understand how enterprise AI moves beyond simple chat interfaces into fully integrated operational platforms. 🧠 What This Video Covers Enterprise AI Is More Than an LLM A common misconception is that AI equals the model. In reality: Model ≠ System The model provides: • reasoning • language understanding • planning • decision support The application provides: • actions • integrations • workflows • business execution Real enterprise AI requires both. Local Inference vs Cloud AI The session compares two primary deployment approaches. Local Inference Models run on: • local servers • private infrastructure • enterprise-controlled environments Advantages: ✅ Privacy ✅ Data sovereignty ✅ Lower long-term inference costs Challenges: ❌ Hardware requirements ❌ Maintenance complexity ❌ Potentially weaker models Cloud APIs Examples include: • OpenAI • Gemini • Anthropic Advantages: ✅ State-of-the-art models ✅ Rapid deployment ✅ Minimal infrastructure Challenges: ❌ Ongoing API costs ❌ Data governance considerations ❌ Third-party dependencies The Role of Tool Calling A major theme throughout the module is: Reasoning vs Action The LLM performs reasoning. The application performs actions. Examples include: • database queries • ERP updates • report generation • sending emails • running calculations Through tool calling, AI becomes capable of interacting with real-world systems. Retrieval Augmented Generation (RAG) Enterprise AI systems often require access to information that was never included during model training. RAG solves this challenge. Question ↓ Document Retrieval ↓ Relevant Context ↓ LLM Reasoning ↓ Answer This enables AI to work with: • accounting policies • contracts • financial statements • internal procedures • audit documentation while reducing hallucinations. Structured Outputs The module explains why enterprise systems require: Structured Outputs rather than unpredictable text. Examples include: • JSON • XML • predefined schemas This allows software systems to reliably process AI-generated outputs. Example: { "customer": "ABC Corporation", "risk_score": 8.4, "action_required": true } Structured outputs are essential for automation. Learned Weights & Inference Engines The session also clarifies key technical concepts. Learned Weights The knowledge stored inside the model. These represent billions of learned relationships developed during training. Inference Engine The runtime environment responsible for: • executing the model • generating responses • serving predictions The inference engine transforms static model weights into useful business outputs. Finance & Accounting Applications These architectural components support: Financial Reporting Agents • retrieve supporting schedules • generate commentary • draft disclosures AP Automation Systems • OCR extraction • vendor validation • workflow routing • ERP integration FP&A Platforms • scenario analysis • forecasting • variance explanations • executive reporting Enterprise Knowledge Systems • policy search • tax research • accounting guidance retrieval • regulatory interpretation 🚀 Why This Matters The future of AI is not: Question → Answer The future is: Question ↓ RAG ↓ Reasoning ↓ Tool Calling ↓ Business Action ↓ Human Review This is the foundation of modern enterprise intelligence systems. DISCLAIMER & LIABILITY NOTICE: The content in this video is for educational and informational purposes only. It does not constitute financial, accounting, tax, or legal advice. No Professional Relationship: Watching this video or interacting in the comments does not create a CPA-Client or fiduciary relationship between you and Sung Lee. Software & Tools: Any code, software, or tools mentioned (including https://www.google.com/search?q=Katchiflow.com) are provided "as-is" for demonstration and drafting purposes only. Outputs should not be relied upon for tax or statutory reporting without independent verification by a qualified professional.
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
Big Data explicado sin humo: el oro del siglo XXI | Javier Morales
ℹ️ Este episodio forma parte del relanzamiento del canal en 2026 (grabado originalmente en 2025) 🎙️ Tech Riders Talks – Temporada 1 Episodio 5 Moderan: Ana María Pereira y Sergio Hierro En este episodio nos metemos de lleno en uno de los pilares fundamentales de la tecnología actual: el Big Data. 💻 Hugo conversa con Javier Morales, director de Inteligencia Artificial en Avanade y profesor universitario, para entender de forma clara y aterrizada qué es realmente el Big Data y cómo está transformando empresas, decisiones y sectores enteros. Hablamos sin filtros sobre: • Qué es realmente el Big Data (más allá del hype) • Cómo se almacenan y procesan datos masivos en la nube • Streaming de datos y plataformas como Hadoop • El valor real del dato en la toma de decisiones empresariales • Casos reales: industria, energía, perfumería y fútbol • Privacidad, redes sociales y uso de nuestros datos • Blockchain y el futuro de la propiedad del dato • Computación cuántica y hacia dónde evoluciona todo esto • Impacto energético y medioambiental del almacenamiento de datos 🚀 Un episodio clave para entender por qué los datos son el verdadero oro del siglo XXI. 👇 Cuéntanos en comentarios: ¿crees que las empresas deberían basar todo en datos o aún hay espacio para la intuición? #TechRiders #TechRidersTalks #PodcastTech #BigData #DataScience #InteligenciaArtificial #IA #MachineLearning #CloudComputing #Hadoop #IoT #Blockchain #ComputacionCuantica #Tecnologia #Innovacion #TransformacionDigital #DataDriven #FuturoDigital
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
9 conceptos fundamentales de Machine Learning | Machine Learning 101
Entra a pyninja.pro y comienza a aprender Machine Learning hoy mismo. En este video veremos 9 conceptos fundamentales de Machine Learning: conjunto de datos, instancia, atributo, etiquetas, algoritmo, hiperparámetros, modelo, sobreajuste y subajuste. Si quieres comenzar a aprender Machine Learning o tienes duda sobre algunos de estos conceptos, este es el video para ti. #datos #instancia #atributo #etiqueta #algoritmo #hiperparametro #modelo #sobreajuste #subajuste Algunos de los algoritmos más utilizados en Machine Learning: 1. Aprende SVM (Support Vector Machines) con Python | Machine Learning 101 https://www.youtube.com/watch?v=pEvLf93kL6s&t=1s 2. Aprende ÁRBOL DE DECISIÓN (Decision Tree) con Python | Machine Learning 101 https://www.youtube.com/watch?v=32-eBE9-4zc 3. Aprende REGRESIÓN LINEAL con Python | Machine Learning 101 https://www.youtube.com/watch?v=hmVh2ddVCK4&t=160s Más videos: - Gradiente Descendente: Batch vs Estocástico vs Mini-batch | Deep Learning 101 https://www.youtube.com/watch?v=Bap0WNIaYHQ&t=218s - Gradiente Descendente con Python | Deep Learning 101 https://www.youtube.com/watch?v=za61eVtq2MY - Open AI Sora: La IA que convierte texto en imágenes https://www.youtube.com/watch?v=n-qSrqIOUrQ&t=18s Temas del video: - Machine Learning - Python - Conceptos fundamentales - Conjunto de datos - Instancia - Atributo - Etiqueta - Algoritmo - Hiperparámetro - Modelo - Sobreajuste - Subajuste Sígueme en: Twitter: https://twitter.com/pyninja_ Contacto: pyninja.official@gmail.com Capítulos: 00:00 - Intro 00:04 - 1. Conjunto de datos 00:52 - 2. Instancia 01:11 - 3. Atributo 01:41 - 4. Etiquetas 02:27 - 5. Algoritmo 02:54 - 6. Hiperparámetros 03:18 - 7. Modelo 04:00 - 8. Sobreajuste 04:22 - 9. Subajuste 04:50 - Conclusión 05:03 - Outro