Videos de Educación
Videos educativos y de formación.
Educación 82 videos
Large Language Models Explained | LLM Basics for Beginners | How ChatGPT Actually Works| Edureka
🔥PGP in Generative AI and ML in collaboration with Illinois Tech: https://www.edureka.co/executive-programs/pgp-generative-ai-machine-learning-certification-training 🔥 Integrated MS+PGP Program in Data Science & AI https://www.edureka.co/dual-certification-programs/ms-data-science-pgp-gen-ai-ml-birchwood In this *Generative AI Course* , we will dive into Generative AI, exploring its definition, key examples, and diverse applications and examples. We'll also discuss the evolution of generative AI, highlight its potential future impact, and showcase an LLM project. Using the Python programming language, streamlit, and Google Gemini API, we will build a healthcare assistant that Analyzes medical diseases like chicken pox, etc. This video is ideal for tech enthusiasts and beginners alike, as it unpacks generative AI's transformative role in the tech world. ✅ 00:00 - Introduction to Generative AI ✅ 01:26 - What is Generative AI? ✅ 02:02 - Generative AI Examples ✅ 02:38 - Applications of Generative AI ✅ 05:01 - Evolution of Generative AI ✅ 05:56 - What is LLM? ✅ 08:04 - Structure of LLM ✅ 10:23 - How LLM Works? ✅ 10:52 - Real Life example of LLM ✅ 11:20 - LLM project ✅Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV 📝Feel free to share your comments below.📝 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 🔵 DevOps Online Training: http://bit.ly/3VkBRUT 🌕 AWS Online Training: http://bit.ly/3ADYwDY 🔵 React Online Training: http://bit.ly/3Vc4yDw 🌕 Tableau Online Training: http://bit.ly/3guTe6J 🔵 Power BI Online Training: http://bit.ly/3VntjMY 🌕 Selenium Online Training: http://bit.ly/3EVDtis 🔵 PMP Online Training: http://bit.ly/3XugO44 🌕 Salesforce Online Training: http://bit.ly/3OsAXDH 🔵 Cybersecurity Online Training: http://bit.ly/3tXgw8t 🌕 Java Online Training: http://bit.ly/3tRxghg 🔵 Big Data Online Training: http://bit.ly/3EvUqP5 🌕 RPA Online Training: http://bit.ly/3GFHKYB 🔵 Python Online Training: http://bit.ly/3Oubt8M 🌕 Azure Online Training: http://bit.ly/3i4P85F 🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐑𝐨𝐥𝐞-𝐁𝐚𝐬𝐞𝐝 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 🔵 DevOps Engineer Masters Program: http://bit.ly/3Oud9PC 🌕 Cloud Architect Masters Program: http://bit.ly/3OvueZy 🔵 Data Scientist Masters Program: http://bit.ly/3tUAOiT 🌕 Big Data Architect Masters Program: http://bit.ly/3tTWT0V 🔵 Machine Learning Engineer Masters Program: http://bit.ly/3AEq4c4 🌕 Business Intelligence Masters Program: http://bit.ly/3UZPqJz 🔵 Python Developer Masters Program: http://bit.ly/3EV6kDv 🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐔𝐧𝐢𝐯𝐞𝐫𝐬𝐢𝐭𝐲 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐬 🔵 Post Graduate Program in DevOps with Purdue University: https://bit.ly/3Ov52lT 🌕 Advanced Certificate Program in Data Science with E&ICT Academy, IIT Guwahati: http://bit.ly/3V7ffrh 🔵 Advanced Certificate Program in Cloud Computing with E&ICT Academy, IIT Guwahati: https://bit.ly/43vmME8 📌𝐓𝐞𝐥𝐞𝐠𝐫𝐚𝐦: https://t.me/edurekaupdates 📌𝐓𝐰𝐢𝐭𝐭𝐞𝐫: https://twitter.com/edurekain 📌𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧: https://www.linkedin.com/company/edureka 📌𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦: https://www.instagram.com/edureka_learning/ 📌𝐅𝐚𝐜𝐞𝐛𝐨𝐨𝐤: https://www.facebook.com/edurekaIN/ 📌𝐒𝐥𝐢𝐝𝐞𝐒𝐡𝐚𝐫𝐞: https://www.slideshare.net/EdurekaIN 📌𝐂𝐚𝐬𝐭𝐛𝐨𝐱: https://castbox.fm/networks/505?country=IN 📌𝐌𝐞𝐞𝐭𝐮𝐩: https://www.meetup.com/edureka/ 📌𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲: https://www.edureka.co/community/ - - - - - - - - - - - - - - What are the Outcomes after learning a Generative AI course? Upon completing the Introduction to Generative AI Fundamentals Course, participants will attain the following learning outcomes: Develop a comprehensive understanding of generative AI, including its principles, applications, and significance across various domains. Gain practical experience through interactive sessions and exercises, enabling participants to apply theoretical concepts to real-world scenarios. Why Learn a Generative AI course? Enroll in the Introduction to Generative AI Fundamentals course to acquire a thorough grasp of generative models, covering everything from basic concepts to cutting-edge uses. Delve into ethical considerations and practical techniques for creating generative AI solutions for actual industry situations, equipping you for various positions in AI advancement and creativity. Who should learn Generative AI courses? This course on Introduction to Generative AI is perfect for individuals aiming to pursue a career in AI, including data scientists, researchers, and developers who wish to explore generative models. It is also beneficial for professionals in various sectors, such as software development, marketing, and retail, who want to utilize AI for problem-solving and innovation. For more information, please write back to us at sales@edureka.in or call us at IND: 9606058406 / US & RoW: +1-8335643323 (toll-free) #genai #genaicourse #generativeai #artificialintelligence #machinelearning #aiexplained #futureofai #aitechnology #deeplearning #aiforbeginners
Generative AI Basics (2026) | How AI Creates Images, Videos & Code
📚Generative AI — AI That Creates | generative ai basics | generative ai tutorial | generative ai for beginners | generative ai course | generative ai examples | generative ai tools | generative ai applications | generative ai explained | generative ai models | generative ai use cases 👉 Welcome to Episode 5 of AI From Zero to Hero — Generative AI explained in simple terms. Learn how Generative AI creates images, videos, music, and code from text prompts. Understand how ChatGPT and large language models work using next-word prediction. Explore AI tools like DALL·E, Midjourney, Stable Diffusion, and Sora. Discover diffusion models and how AI generates realistic images from noise. Master prompt engineering to get better AI results and improve productivity. See how GitHub Copilot helps developers and IT admins write code faster. Learn real-world AI use cases in marketing, content creation, and automation. Understand risks like deepfakes, voice cloning, and AI ethics. Perfect beginner guide to Generative AI, AI tools, and future technology trends. 🏷️ Tags #ChanderManiPandey #GenerativeAI #GenerativeAIBasics #GenerativeAITutorial #GenerativeAIForBeginners #GenerativeAICourse #GenerativeAIExamples #GenerativeAITools #GenerativeAIApplications #GenerativeAIExplained #GenerativeAIModels
¿Que son los Large Language Models (LLM)?
#llm #inteligenciaartificial #ia #agentesia En este video veremos una introducción a los Large Language Models (LLM) o Grandes Modelos de Lenguaje. Veremos que son, como funcionan, para que se usan, como se entrenan, que tipos hay y cuales son los principales retos de estos modelos en la actualidad.
Historia y evolución de la IA: de reglas a agentes (explicado fácil)
La inteligencia artificial no empezó con ChatGPT. En este video te explico la historia y evolución de la IA, desde sus orígenes con Alan Turing hasta la era actual de agentes, modelos fundacionales y sistemas que empiezan a trabajar por nosotros. Vas a entender de forma simple: - cómo partimos con sistemas basados en reglas - por qué ese enfoque no escaló - cómo nace el machine learning - qué cambió con las redes neuronales y el deep learning - por qué AlphaGo fue un punto de inflexión - cómo los transformers revolucionaron el lenguaje - qué hay detrás de GPT y ChatGPT - y hacia dónde vamos con agentes, MCP y sistemas que ejecutan tareas Link al paper de turing: https://courses.cs.umbc.edu/471/papers/turing.pdf Link paper transformers: https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf
¿Qué es la Inteligencia Artificia?l: explicación fácil en 14 minutos
La Inteligencia Artificial ya está en tu móvil, en YouTube, en Netflix, en asistentes virtuales, en coches autónomos y en muchas herramientas de trabajo. Pero, ¿qué es realmente la IA? En este video te explico de forma clara y visual qué es la Inteligencia Artificial, cómo funciona el Machine Learning, qué son las redes neuronales, dónde se usa hoy y cuáles son sus beneficios y riesgos. Aprenderás: Qué significa realmente “Inteligencia Artificial” Cómo aprende una máquina a partir de datos Qué es el Machine Learning Qué son las redes neuronales, explicado fácil Ejemplos reales de IA en la vida diaria Beneficios de la IA: automatización, eficiencia y productividad Riesgos de la IA: ética, empleo, privacidad y sesgos Por qué la IA no es magia, sino patrones, datos y decisiones humanas La IA no reemplaza la pregunta humana. La hace más importante. Si quieres entender la tecnología que está cambiando el mundo sin complicarte con tecnicismos, este video es para ti. Suscríbete para más explicaciones claras sobre tecnología, inteligencia artificial y futuro digital.
How to Create AI Image Prompt Enhancer.
AI Movie Film Making Course. Check link here https://sl1nk.com/evxzmef 🚀 How to Create AI Prompt Image Enhancer (Step-by-Step Guide) Want to turn basic prompts into high-quality, cinematic AI images? In this video, you’ll learn how to build your own AI prompt image enhancer that transforms simple ideas into stunning visuals for tools like MidJourney, DALL·E, and Stable Diffusion. Whether you're a content creator, designer, or AI filmmaker, this method will upgrade your prompts and give you better, sharper, and more realistic results instantly. 🔥 What You’ll Learn in This Video: How AI image prompts actually work. The secret structure of powerful prompts How to enhance low-quality prompts into pro-level results. Prompt formulas for cinematic, realistic, and artistic images. How to automate prompt enhancement for faster workflow 💡 Why This Matters: Most people get bad AI images because their prompts are weak. This tutorial shows you how to fix that and create prompts that generate viral, scroll-stopping visuals every time. 🛠️ Tools & Concepts Covered: AI Image Generation Techniques Prompt Engineering Strategies Image-to-Image Enhancement Style Keywords, Lighting, Camera Angles. 🎯 Perfect For: YouTubers & Content Creators AI Filmmakers Graphic Designers Beginners & Advanced Users Use this prompt enhancer to create YouTube thumbnails, movie scenes, ads, and viral content faster than ever
LLAMA CPP ⚙️ Domina los Parámetros de Sampleo para un LLM PERFECTO
Descubre cómo funcionan los parámetros de sampleo en los modelos de lenguaje y aprende a configurarlos para optimizar las respuestas de tu IA. En este video analizamos a fondo los ajustes de sampleo, tomando como referencia llama.cpp y LM Studio, aunque estos conceptos aplican a la mayoría de los cargadores de modelos como vLLM. Exploramos la naturaleza de los LLM como predictores de tokens y cómo el contexto influye en la generación de cada palabra. Explicamos detalladamente el impacto de la temperatura en la creatividad y la precisión, la función del Top K para limitar el rango de tokens probables y las diversas penalizaciones por repetición (repeat penalty, presencia y frecuencia) para evitar que el modelo caiga en bucles infinitos. Finalmente, discutimos cómo adaptar estos ajustes según la tarea, ya sea para razonamiento complejo o para la escritura de código. 📝 Índice: 00:00:00 Introducción a los parámetros de sampleo 00:00:51 Funcionamiento del contexto y predicción de tokens 00:03:12 Aleatoriedad vs Probabilidad en las respuestas 00:04:43 La Temperatura y la creatividad del modelo 00:07:31 Top K y la limitación de tokens 00:08:17 Prevención de repeticiones y bucles (Penalty) 00:10:35 Parámetros adicionales en llama.cpp 00:12:00 Configuración según el tipo de uso (Código vs Texto) #inteligenciaartificial #LLM #llamaCPP #LMStudio #MachineLearning #IA #PromptEngineering #Tecnologia Contacto: nichonauta@gmail.com Web: nichonauta.com URL del Directo Completo: https://www.youtube.com/watch?v=WTYkns3r7h8
Convolutional Neural Networks (CNNs) Explained
This video goes over Convolutional Neural Networks (CNNs), giving a high level explanation of how computers identify objects. Link to article: https://linguisticmaz.medium.com/convolutional-neural-networks-explained-b42f12e66de0?sk=b36aa00535b7bb3c1b5fff86798ea44e Time stamps: 0:00 - How computers see 1:04 - Why not a Neural Network 1:41 - How computers identify objects 2:07 - Kernels and Filters 4:18 - Activation functions (ReLu) 4:46 - Padding 6:50 - Pooling 7:52 - CNN Architecture 8:48 - Dealing with rotation and scale 9:05 - Summary Subscribe to my new weekly newsletter where I keep you up to date with the applications of data science and AI in Medicine: https://thehealthalgorithm.substack.com/ Let's Connect: https://www.linkedin.com/in/mazen-ahmed-8972aa160/
Turn Prompts into Stunning Images with AI
In this tutorial, you’ll learn how to create images using AI prompts and turn your ideas into stunning visuals in seconds. AI image generators allow you to describe what you want using text, and the AI will generate high-quality images, artwork, and designs based on your prompt. Popular tools like DALL·E, Midjourney, and Stable Diffusion make it easy to create visuals for thumbnails, social media, presentations, and creative projects. In this video, you’ll learn how to: • Write effective prompts for AI image generation • Generate images from text descriptions • Customize styles, lighting, and composition • Improve image quality with better prompts • Use AI images for different purposes This method is perfect for content creators, designers, marketers, educators, and students who want to create professional visuals quickly without advanced design skills. If you want to turn your ideas into visuals using AI prompts, this tutorial will guide you step by step. #AIImages #TextToImage #AITools #PromptEngineering #ContentCreation
LLM 🤖 Cómo FUNCIONAN realmente los Modelos de Lenguaje
Descubre cómo funcionan realmente los modelos de lenguaje y desmitifica la idea de que poseen autonomía. En este video exploramos la mecánica técnica detrás de la predicción de texto, el uso de plantillas y cómo los LLM interactúan con herramientas externas. Analizamos el proceso de autocompletado y la estructura de los prompts, diferenciando entre el system prompt, el usuario y el asistente. A través de ejemplos prácticos con llama CPP, se explica que la IA no está teniendo una conversación real, sino calculando la siguiente parte más probable de un texto basándose en fórmulas y pesos matemáticos. También profundizamos en la cadena de razonamiento (reasoning) y el uso de herramientas (tools). Explicamos que cuando un modelo como GitHub Copilot crea un archivo, no está haciendo clic en un botón, sino prediciendo un comando específico que el programa externo luego ejecuta. Finalmente, comparamos el rendimiento de modelos como Gema y Qwen, analizando cómo la cuantización y el tamaño del modelo afectan su capacidad para ejecutar tareas técnicas. 📝 Índice: 00:00:00 ¿Cómo funcionan los modelos de lenguaje? 00:01:12 Estructura de prompts: System, User y Assistant 00:02:30 El proceso de predicción de texto y tokens 00:04:00 Cadenas de razonamiento y etiquetas de pensamiento 00:06:15 El uso de herramientas y comandos externos 00:08:00 Análisis de modelos: Gema, Qwen y cuantización #InteligenciaArtificial #LLM #Programacion #MachineLearning #GithubCopilot #Tecnologia #IA #ModelosDeLenguaje Contacto: nichonauta@gmail.com Web: nichonauta.com URL del Directo Completo: https://www.youtube.com/watch?v=zUUb_rMjzxU
How AI Knows It's Wrong | Loss Function Explained
Ever wondered how a Neural Network actually "learns" from its mistakes? In this video, we break down the Loss Function, the essential "Report Card" that tells an AI model exactly how wrong it is. We move beyond complex formulas to explain the intuition behind: The Error Gap: Using a house price example (predicting 48L vs. an actual 50L) to visualize how we measure mistakes. Mean Squared Error (MSE): Why we square errors to penalize big mistakes more heavily than small ones. Cross-Entropy: How we measure confidence in classification, like predicting a 90% chance of a football goal. The Learning Loop: How the model uses loss to update its weights and improve over time. Whether you are a beginner in Data Science or just curious about how AI works, this guide simplifies the core feedback loop that makes Machine Learning possible. Keywords Primary Keywords: Loss Functions Explained, Neural Network Training, Machine Learning for Beginners, Mean Squared Error Intuition, Cross-Entropy Loss, AI Learning Process, Gradient Descent Basics, Regression vs Classification. Secondary Keywords: How AI learns, Neural Network weights and biases, AI report card analogy, house price prediction AI, MSE vs Cross Entropy, deep learning fundamentals. Hashtags #AI #MachineLearning #DeepLearning #DataScience #NeuralNetworks #LossFunction #TechExplained #LearnAI #PythonProgramming #AITutorial
Without This, AI Is Dumb | Activation Functions Explained
Without activation functions, even the deepest neural network is just doing simple math. In this video, we break down activation functions in the simplest way possible and understand why they are the real reason AI can learn complex patterns instead of just drawing straight lines. We start from the basics of an artificial neuron, then move step by step into how non-linearity changes everything in a neural network. You’ll clearly understand: Why linear models fail in AI What activation functions actually do How Sigmoid, ReLU, and Softmax work Why Sigmoid causes slow learning (vanishing gradient problem) Why ReLU is widely used in deep learning How Softmax helps in multi-class classification We also use simple real-world examples so you can connect concepts easily, whether you are preparing for interviews, learning machine learning, or just starting your AI journey. If you’ve ever wondered how AI actually becomes “intelligent,” this is the missing piece. Next, we’ll cover loss functions and how AI learns from its mistakes. Keywords (naturally included): activation functions, neural networks, deep learning, machine learning, artificial neuron, sigmoid function, relu function, softmax function, non linearity in neural networks, vanishing gradient problem, ai basics, deep learning explained, neural network tutorial, ai for beginners Hashtags: #AI #MachineLearning #DeepLearning #NeuralNetworks #ActivationFunctions #ArtificialIntelligence #ReLU #Sigmoid #Softmax #AIBasics #DataScience #LearnAI #AIExplained #MLBasics #TechExplained