Videos de python
Videos etiquetados con "python"
python 17 videos
Cómo crear agentes de IA con voz y visión en Python
Aprende a construir agentes de inteligencia artificial que ven, escuchan y hablan en tiempo real usando Python. Tutorial paso a paso con Vision-Agents de Stream. En este tutorial aprenderás: - Como instalar Vision-Agents con un solo comando - Como crear un agente de voz que te escucha y te responde - Como añadir herramientas MCP para que el agente haga tareas - Como conectar visión por computadora con YOLO para que el agente vea Codigo y comandos usados en el video: - pip install vision-agents - Agent + getstream.Edge + openai.Realtime - YOLOPoseProcessor + Gemini Realtime Ideal para: desarrolladores Python, estudiantes, entusiastas de IA 🎬 Mira también: Cómo automatizar tareas complejas con agentes de IA en Python https://youtube.com/watch?v=s3PFQcxwuUU Suscribete para mas Python + IA: https://youtube.com/@lytohlgai #python #agentesia #visionagents #ia #github #opensource #devtools #realtime #computer vision
Master RAG in 6 Minutes
# RAG Explained: How AI Retrieves the Right Information Learn **Retrieval-Augmented Generation (RAG)** explained in simple terms. In this beginner-friendly tutorial, you'll understand how RAG works, why **Large Language Models (LLMs)** use it, and how AI systems retrieve relevant information before generating accurate, up-to-date responses. RAG is one of the most important technologies behind modern AI applications. It enables AI assistants to answer questions using external knowledge, documents, databases, and the latest information instead of relying only on what they learned during training. ### In this video, you'll learn: * What is Retrieval-Augmented Generation (RAG)? * Why Large Language Models (LLMs) need RAG * How RAG retrieves relevant information before generating an answer * The complete RAG architecture and workflow explained step by step * Real-world examples of RAG applications * Benefits and limitations of RAG * RAG vs Fine-Tuning: Which approach should you use? Whether you're a student, developer, AI engineer, data scientist, or machine learning enthusiast, this tutorial will help you understand one of the core building blocks of modern AI systems. This video also covers important AI concepts including: * Large Language Models (LLMs) * Generative AI * AI Agents * Vector Databases * Embeddings * Semantic Search * Knowledge Retrieval * Context Augmentation * Prompt Engineering * AI Application Development If you found this video helpful, please Like, Subscribe, and Share it with others who are learning Artificial Intelligence and Machine Learning. Subscribe for more beginner-friendly AI tutorials covering: * AI Agents * Retrieval-Augmented Generation (RAG) * Large Language Models (LLMs) * Model Context Protocol (MCP) * Prompt Engineering * Vector Databases * AI Engineering * Python for AI * Machine Learning * Generative AI * Open Source AI * AI Tools and Tutorials #RAG #AI #LLM
Deep Learning Fundamentals | Neural Networks, Layers & AI Applications
🚀 Dive into the world of Deep Learning and understand the technology powering ChatGPT, self-driving cars, and modern AI systems. 📚 Topics Covered: ✅ What is Deep Learning? ✅ Machine Learning vs Deep Learning ✅ Deep Neural Networks ✅ Hidden Layers Explained ✅ Training Deep Learning Models ✅ Activation Functions ✅ Loss Functions & Optimization ✅ Deep Learning Workflow ✅ Real-World Applications ✅ Mini Project ✅ Interview Questions ✅ Quiz ✅ Assignment 🎯 Perfect For: AI Enthusiasts Data Science Students Machine Learning Beginners Software Developers
Anthropic Just Dropped Their Internal Data Playbook (copy this)
Anthropic just dropped their entire internal data playbook. Here's what they're doing and how it affects your career. 💌 Join 10k+ aspiring data analysts & get my tips in your inbox weekly 👉 https://dcj.app/newsletter-DTgIjn 🆘 Feeling stuck in your data journey? Come to my next free "How to Land Your First Data Job" training 👉 https://dcj.app/training-DTgIjn 👩💻 Want to land a data job in less than 90 days? 👉 https://dcj.app/daa-DTgIjn 👔 Ace The Interview with Confidence 👉 https://dcj.app/interviewsimulator-DTgIjn 📄 Read Anthropic's full data playbook 👉 https://claude.com/blog/how-anthropic-enables-self-service-data-analytics-with-claude ⌚ TIMESTAMPS 00:00 – Anthropic dropped their data playbook 02:39 – Why AI analytics keeps failing 05:24 – How they hit 95% accuracy 09:24 – What a Claude skill is 14:39 – None of this is actually new 17:09 – Still hiring data people 🔗 CONNECT WITH AVERY 🎥 YouTube Channel: https://dcj.app/youtube-averysmith-DTgIjn 🤝 LinkedIn: https://linkedin.com/in/averyjsmith/ 📸 Instagram: https://instagram.com/datacareerjumpstart 🎵 TikTok: https://tiktok.com/@verydata 💻 Website: https://dcj.app/datacareerjumpstart-DTgIjn
Creé un coach de sentadillas con IA en 5 minutos | Visión computacional explicada
¿Y si tu cámara pudiera ayudarte con los ejercicios? En este video te muestro cómo creé un coach de sentadillas con visión computacional que cuenta tus repeticiones en tiempo real. Sin código complicado — solo la lógica, las tecnologías y cómo funciona detrás de cámaras. 📌 Lo que verás en este video: 00:00 - Introducción: ¿Y si tu cámara pudiera...? 00:15 - Demo en vivo del coach funcionando 00:30 - Cómo la computadora ve tu cuerpo (YOLOv8) 01:38 - La matemática del ángulo de rodilla 02:25 - Los 3 estados (arriba, bajando, abajo) 03:28 - Arquitectura: cámara → cerebro → voz → orquestador 04:19 - Cierre y próximos proyectos 💻 Código y documentación completa: https://github .com/PipetoBlack/coachSentadilla ────────────────────────────── 🔔 Suscríbete para no perderte los próximos laboratorios 📸 Instagram: @pipetolab ────────────────────────────── #VisionComputacional #Python #YOLO #OpenCV #InteligenciaArtificial #MachineLearning #DeepLearning #ComputerVision #PipetoLab #AprendizajeAutomático #IA #TutorialPython #PoseEstimation #Automacion
The Math Behind Deepfakes (GANs Explained)
Discover the math behind deepfakes and how Generative Adversarial Networks (GANs) changed AI forever. Before 2014, teaching a machine to create highly realistic data from scratch involved incredibly slow and complex probabilistic calculations. That all changed when Ian Goodfellow and his team introduced a completely new paradigm. By pitting two neural networks against each other in a continuous Minimax game, they bypassed the heavy calculus and unlocked the modern era of generative AI. In this video, we break down the exact mechanics of GANs. You will learn how the Generator acts as a counterfeiter trying to create perfect fakes, while the Discriminator acts as the police trying to catch them. We also dive into the training loop, the significance of the Jensen-Shannon divergence, and the mathematical proof that guarantees these models can perfectly mimic reality. 00:00 - The dense math of early generative models 00:58 - Re-framing generation as an adversarial game 02:27 - The Counterfeiter and the Police analogy 03:11 - The Minimax game and training loop steps 04:24 - Visualizing the push and pull of data distribution 05:58 - Proving the perfect fake mathematically 06:40 - The Jensen-Shannon divergence explained 07:31 - Why GANs matter for modern AI and deepfakes 🔗 Stay Connected 👉 Subscribe on YouTube: https://www.youtube.com/@insightforge_9 👉 Read the Blog (AI, Chatbots & Automation): https://insightforge-ai.blogspot.com/ 👉 Connect on LinkedIn: https://www.linkedin.com/in/mohit-rathod-7991241b5/ 👉 Join the Newsletter: https://www.linkedin.com/newsletters/7330620395449937920/ 👉 Follow on Instagram: https://www.instagram.com/insightforge.ai/ #GenerativeAI #MachineLearning #Deepfakes
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
NumPy en Python 🐍 | Arreglos, Matrices y Operaciones Vectorizadas desde Cero
En este video aprenderás a dominar NumPy, la librería fundamental de Python para cálculo numérico y manejo de matrices. Descubrirás cómo crear arreglos unidimensionales y matrices, realizar operaciones vectorizadas de forma eficiente, y trabajar con índices y slicing. 📌 Temas que verás: 🔹 INTRODUCCIÓN A NUMPY: ✅ ¿Qué es NumPy y para qué sirve? ✅ Importación con alias: import numpy as np ✅ Diferencia entre vectores NumPy y listas de Python 🔹 ARREGLOS UNIDIMENSIONALES: ✅ Creación con np.arange() y np.array() ✅ Operaciones matemáticas directas: suma, resta, multiplicación ✅ Funciones estadísticas: np.sum(), np.mean() ✅ Índices y slicing: [inicio:final], [:], [1:] ✅ Vectores especiales: np.zeros(), np.ones() 🔹 MATRICES (ARREGLOS 2D): ✅ Creación con listas de listas ✅ Función shape para obtener dimensiones (filas, columnas) ✅ Matrices especiales: np.zeros(), np.ones(), np.eye() ✅ Acceso a elementos: matriz[fila, columna] ✅ Slicing de filas y columnas: matriz[1,:], matriz[:,0] ✅ Modificación de elementos 🔹 OPERACIONES AVANZADAS: ✅ Funciones matemáticas: np.sin(), np.random.rand() ✅ Multiplicación elemento a elemento con * ✅ Producto punto de matrices con np.dot() ✅ Suma por ejes: sum(axis=0), sum(axis=1) ✅ Extracción de diagonal con A.diagonal() 💻 Ideal para quienes inician en Machine Learning con Python. 🔔 ¡Suscríbete y activa la campanita para no perderte el siguiente video! #Python #NumPy #ArreglosPython #MatricesPython #MachineLearning #Programacion #CursoPython #PythonParaPrincipiantes #AprendePython #Codigo #DataScience #InteligenciaArtificial #TutorialPython #PythonBasico #OperacionesVectorizadas #CalculoNumerico
Funciones en Python 🐍 | def, return, *args, kwargs y Más desde Cero
En este video aprenderás a dominar las funciones en Python, la herramienta más poderosa para reutilizar código, generalizar tareas y escribir programas más limpios y profesionales. Cubriremos desde la definición básica con def hasta el uso avanzado de *args y **kwargs. 📌 Temas que verás: 🔹 CONCEPTOS BÁSICOS: ✅ Definición de funciones con def y uso de return ✅ Ejemplo práctico: función pitágoras para calcular la hipotenusa ✅ Tipado dinámico en funciones (mismos parámetros, distintos tipos) ✅ Docstrings: documentación automática con ''' ''' 🔹 FLEXIBILIDAD DE FUNCIONES: ✅ Funciones sin parámetros de entrada ✅ Funciones sin retorno (devuelven None) ✅ Parámetros opcionales con valores por defecto ✅ Múltiples sentencias return en una misma función 🔹 PASO DE PARÁMETROS: ✅ Paso por valor (tipos simples: int, float, str, bool) ✅ Paso por referencia (tipos compuestos: listas, diccionarios) ✅ Cómo proteger los datos originales usando copias lista[:] 🔹 PARÁMETROS AVANZADOS: ✅ *args: argumentos posicionales variables (tupla) ✅ **kwargs: argumentos con nombre (diccionario) ✅ Combinación de *args y **kwargs en una misma función ✅ Orden obligatorio en la definición 💻 Ideal para quienes inician en Machine Learning con Python. 🔔 ¡Suscríbete y activa la campanita para no perderte el siguiente video! #Python #FuncionesPython #DefPython #ArgsKwargs #MachineLearning #Programacion #CursoPython #PythonParaPrincipiantes #AprendePython #Codigo #DataScience #InteligenciaArtificial #TutorialPython #PythonBasico #Docstrings
Variables en Python: Aprende a Almacenar Datos desde Cero 🐍 | Curso Machine Learning con Python
En este video aprenderás qué son las variables en Python, cómo almacenar valores en la memoria del computador y las reglas de nomenclatura que debes respetar. Cubriremos los tipos numéricos más importantes: enteros (int) y decimales (float), además de realizar operaciones básicas como suma, resta, multiplicación, potenciación y división. 📌 Temas que verás: ✅ Qué es una variable y para qué sirve ✅ Reglas de nomenclatura en Python ✅ Tipado dinámico y asignación con = ✅ Variables numéricas: int y float ✅ Operadores: +, -, *, **, /, //, % ✅ Uso de la librería math (pi, factorial, seno, etc.) ✅ Funciones round(), ceil(), trunc() 💻 Ideal para principiantes que inician en Machine Learning con Python. 🔔 ¡Suscríbete y activa la campanita para no perderte la siguiente clase! #Python #VariablesEnPython #MachineLearning #Programacion #CursoPython #PythonParaPrincipiantes #AprendePython #Codigo #DesarrolloWeb #DataScience #InteligenciaArtificial #TutorialPython #PythonBasico #NumerosEnPython #TiposDeDatos
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
The AI Factory: Engineering Modern LLM Inference Pipelines | Uplatz
Modern AI systems are no longer simple models running isolated predictions—they operate like massive digital factories processing billions of requests, orchestrating GPUs, managing memory, and delivering intelligent responses at global scale. In this video, we explore “The AI Factory” and break down how modern LLM inference pipelines are engineered for performance, scalability, and efficiency. This video is by Uplatz. You’ll learn how large language model inference works behind the scenes, from token generation and request routing to distributed GPU execution and response optimization. We explain why inference engineering has become one of the most critical challenges in the generative AI era. The video dives into core components of modern inference pipelines including model serving, batching strategies, KV cache management, GPU scheduling, vector databases, retrieval-augmented generation (RAG), and low-latency orchestration systems. You’ll understand how organizations optimize infrastructure to reduce inference costs while maintaining performance and responsiveness. We also explore technologies and frameworks used in production AI systems such as Kubernetes, Ray, and vLLM for scalable AI deployment and inference acceleration. Additionally, we discuss concepts like model quantization, mixture-of-experts (MoE) architectures, inference parallelism, autoscaling, observability, and AI infrastructure optimization. Learn how companies engineer AI platforms capable of serving millions of users across enterprise applications, copilots, AI agents, and multimodal systems. Whether you're an AI engineer, platform architect, DevOps professional, cloud engineer, researcher, or technology enthusiast, this video provides a practical and structured understanding of how modern LLM inference factories operate at scale. For full course browse https://uplatz.com/online-courses #LLM #GenerativeAI #AIInfrastructure #MLOps #LLMOps #ArtificialIntelligence #vLLM #GPUComputing #AIEngineering #Uplatz ---------------------------------------------- 🌐 Welcome to Uplatz – Your Gateway to Career Transformation! To access full courses or training bundles: 🌐 https://uplatz.com 📧 support@uplatz.com 🎓 About Uplatz Uplatz is a global leader in online IT and professional training, offering comprehensive courses in AI, machine learning, data science, cloud computing, cybersecurity, and enterprise technologies such as SAP, Oracle, Salesforce, and ServiceNow. With expert-led programs and real-world learning paths, Uplatz empowers learners and organizations across 190+ countries to build future-ready skills and thrive in the digital era. 📘 Explore Uplatz Course Portfolio Learn the most in-demand and emerging technologies with Uplatz: ✅ AI & Machine Learning – Agentic AI, LLMs, LangChain, Deep Learning, MLOps, LLMOps ✅ Cloud & DevOps – AWS, Azure, GCP, Docker, Kubernetes, Terraform, CI/CD ✅ Data & Analytics – Data Science, Data Engineering, Power BI, Tableau, Big Data (Spark, Kafka) ✅ Programming & Frameworks – Python, FastAPI, Django, Java, JavaScript, SQL ✅ Cybersecurity & Blockchain – Ethical Hacking, Cloud Security, Zero Trust, Blockchain & Web3 ✅ IoT & Embedded Systems – IoT Platforms, Edge Computing, Embedded C, Microcontrollers ✅ ERP & CRM – SAP (all modules), Salesforce, Oracle ERP, Microsoft Dynamics ✅ Web & App Development – Full-Stack Development, React, Angular, Node.js, Flutter 🎓 Master cutting-edge skills. Build your tech career with Uplatz. 🌐 Learn more: https://uplatz.com 🎯 Why Choose Uplatz ✔️ Job-focused, project-based learning ✔️ Globally recognized certifications ✔️ Lifetime access & affordable pricing ✔️ Career guidance and mentorship 🔔 Subscribe for weekly tech tutorials, demos, and success stories. 📲 Follow us on LinkedIn, Instagram, Twitter, and Facebook. #Uplatz #Tech #Technology #MachineLearning #CloudComputing #Learning