Videos de Educación

Build a Quantum Support Vector Machine From Scratch(Qiskit Simulation Tutorial)!

Can Quantum Computers actually improve AI, or is it all just hype? In this step-by-step tutorial, we move past the raw physics theory and build a real-world Quantum Machine Learning (QML) pipeline from scratch. We will use Python and IBM's Qiskit stack to construct a Quantum Support Vector Classifier (QSVC). You’ll see exactly how classical data is mapped into high-dimensional quantum state space (Hilbert Space) using a ZZFeatureMap, and how we extract quantum advantage using quantum kernel estimation. 💻 GET THE CODE FROM THIS VIDEO: [Insert GitHub Link / Kaggle Notebook Link Here] 👇 TIMESTAMPS 1 - The Reality of Quantum Machine Learning 2 - How Quantum Feature Maps & Kernels Work 4 - Setting Up Your Environment (Pip Install Stack) 5 - Step 1: Preparing & Scaling Classical Data for Qubits 6 - Step 2: Coding the ZZFeature Map & Entanglement Circuits 7 - Step 3: Training the Quantum Classifier (QSVC) 9 - Step 4: Testing, Accuracy Check, & Visualizing Boundaries 10 - How to Run This on Real IBM Quantum Hardware If you have questions about quantum data loading bottlenecks or setting up Qiskit, drop them in the comments below! Don't forget to like and subscribe for more real-world Quantum AI guides. #QuantumComputing #MachineLearning #QuantumAI #Python #Qiskit #DataScience 💡 Support Us and Stay Connected! 🌟 Exclusive Access: Join our channel for premium content and resources:https://www.youtube.com/channel/UCsFz0IGS9qFcwrh7a91juPg/join. 💬 Join Our Discussion Groups: 📱 Telegram: https://epythonlab.t.me/ 🌐 Facebook: https://facebook.com/epythonlab1/ ✨ We Look Forward to Seeing You Again! ✨

04 How Large Language Models (LLMs) Works? | All about LLMs | What are Tokens & Context Length?

Generative AI | LLM | GenAI | NN | Large Language Models ⏰ Scheduled to be Public from Members Only on 01st Jun 2026 16:00 HRS IST ⏰ ===== In this video, you will learn ===== What is Large Language Model? What is LLM? How LLMs work? Next Token Prediction in LLM, What are Tokens and Context Length? Importance on Tokens in LLM, Different Sampling Controls, LLM Personas and Prompts, Probability Distribution for LLMs ===== Chapters ===== 00:00 - Introduction 00:27 - What are Large Language Models or LLMs? 03:44 - How Large is Large in LLMs? 05:44 - Transformers 08:07 - What are Tokens and their Importance in LLM? 08:18 - What is Vocabulary in LLM? 14:27 - Probability Distribution for Tokens 19:01 - Sampling Controls - Temperature, Top-p 25:51 - Auto-Regressive Generation Loop 28:53 - How LLMs preserves meaning? 30:58 - How LLMs are Trained? 33:15 - What is Fine Tuning? 34:28 - LLM Personas/Roles and Prompts 36:55 - What is Context Length? 39:01 - Model Knowledge Cutoff and Hallucination 41:23 - Open and Closed LLM Models 42:36 - Reasoning Models 43:24 - Multimodal Models ===== Links ===== Google's "Attention is all You Need" Paper - https://arxiv.org/pdf/1706.03762 Groq Cloud - https://console.groq.com/home GPT Tokenizer - https://platform.openai.com/tokenizer ===== Other Playlists ===== Checkout all other playlists on Data Engineering 👇🏻 https://www.youtube.com/@easewithdata/playlists ===== GitHub Repo ===== https://github.com/subhamkharwal ===== Connect with ME ===== LinkedIn - https://www.linkedin.com/in/subhamkharwal Medium - https://subhamkharwal.medium.com ===== Hashtags ==== #genai #dataengineering #python #agenticai #aiagents #aiagent #nn #neuralnetworks

Explicación del Proyecto - Machine Learning (2026)

Explicación del Informe - Machine Learning

What Is AI? A Plain-English Introduction for Developers | Mastering AI & ML Course M1:E1

Detailed Description Artificial Intelligence is everywhere right now... but what does it actually mean? In this first episode of the Master AI & ML series, we strip away the buzzwords and explain AI in plain language for developers, technologists, creators, and curious learners. No PhD required. No math overload. Just a practical mental model that helps you understand what AI really is and why it matters. You’ll learn the fundamental difference between traditional programming and machine learning, how AI systems are trained, why modern AI feels so powerful, and where today’s tools actually fit on the spectrum between Narrow AI and AGI. This episode is designed to give you a rock-solid foundation before diving deeper into machine learning, deep learning, neural networks, and real-world AI engineering workflows. 🚀 In this episode: What AI actually is in plain English Rule-based software vs machine learning Why AI “learns” instead of following hard-coded rules Narrow AI vs AGI explained clearly How training and inference work Why 2012 changed everything in AI Real-world examples like spam filters and recommendation systems The biggest misconceptions developers have about AI Why this AI wave is genuinely different Whether you're a software engineer, product manager, founder, analyst, student, or just AI-curious, this series is built to help you understand the field from first principles and connect theory to real-world applications. 🧠 This is Episode 1 of the Master AI & ML series. 🎯 Coming Next: M1:E2 — AI vs. ML vs. Deep Learning We’ll break down the differences between these terms and explain why the distinctions actually matter when building products and systems. 👇 Drop a comment: What was the first AI system you remember using? Spam filters? Netflix recommendations? Siri? Something else entirely? 🔔 Subscribe for future episodes covering: Python for AI Machine Learning Foundations Neural Networks Deep Learning NLP & LLMs AI APIs and Product Integration AI Project Deployment Real-world AI Engineering CTA 👍 Like the video if it helped clarify AI without the hype 💬 Share your first memorable AI experience in the comments 🔔 Subscribe to follow the complete AI & ML learning journey 🚀 New episodes weekly as we build from fundamentals to production-ready AI systems Tags AI, Artificial Intelligence, What Is AI, AI Explained, Machine Learning, Deep Learning, AI for Developers, Intro to AI, Beginner AI Course, AI Tutorial, Artificial Intelligence Tutorial, AI Fundamentals, Neural Networks, AI Training, Narrow AI, AGI, AI Course, AI Engineering, AI Education, Learn AI, Generative AI, GPT, LLM, AI Concepts, AI Basics, Technology Explained, Python AI, Software Engineering, Data Science, AI and ML, AI Video Course, AI for Beginners, Developer Education, Future of AI, AI Systems Hashtags #AI #MachineLearning #DeepLearning #ArtificialIntelligence #AIForDevelopers #LearnAI #GenerativeAI #TechEducation #NeuralNetworks #DataScience

Master AI & Machine Learning — Course Overview | What You’ll Learn & Who It’s For

This is the course overview for Master AI & Machine Learning — a 35-episode, 6-module video series for developers, analysts, and technical professionals who want to go from AI-curious to AI-capable. No fluff. Real tools. Hands-on builds. This is the course overview for Master AI & Machine Learning — a 35-episode, 6-module video series for developers, analysts, and technical professionals who want to go from AI-curious to AI-capable. No fluff. Real tools. Hands-on builds. In this video you’ll learn: • Who this course is built for — and who it’s not • What you’ll be able to build and do by the end • How all 6 modules connect from foundations to strategy • A preview of the hands-on demos across the series ─── WHAT YOU’LL LEARN IN THIS SERIES ─── • Module 1 — AI Foundations: what AI is, how machines learn, myth vs. reality • Module 2 — Data Essentials: data types, cleaning, bias, building datasets • Module 3 — Core ML Algorithms: regression, classification, clustering, neural networks • Module 4 — AI Tools & Platforms: no-code AI, generative AI, workflow automation • Module 5 — Building with AI: prompt engineering, RAG, fine-tuning, chatbot builds • Module 6 — Ethics, Strategy & Future: governance, AI strategy, what’s coming next ─── WHO THIS IS FOR ─── • Developers who write code but haven’t worked with ML frameworks • Analysts and data-adjacent professionals adding AI to their toolkit • IT and tech professionals who want to demystify the AI black box Not the right fit: experienced ML engineers, or anyone looking for advanced math / architecture theory. That’s a different series. ─── ABOUT THIS SERIES ─── Master AI & Machine Learning is a Series of Thoughts production, presented by TechnovativeAI — an AI consulting firm helping businesses implement AI that actually works. This course is the distilled version of what we see working in the field. ─── LINKS & RESOURCES ─── TechnovativeAI: www.technovativeai.com Series of Thoughts: www.seriesofthoughts.com Full course playlist: AI course for beginners, machine learning tutorial, AI and ML explained, learn AI 2025, AI for developers, machine learning for beginners, deep learning basics, artificial intelligence course, AI tools tutorial, prompt engineering, RAG tutorial, AI implementation, TechnovativeAI, Series of Thoughts, AI training series, ML course YouTube, AI fundamentals, generative AI course, AI workflow automation, applied machine learning

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

¿Cómo Funciona ChatGPT? La Explicación Más Clara de los LLM, Transformers y Embeddings

¿Qué es un LLM? ¿Cómo funciona ChatGPT? ¿Qué son los Transformers, los Embeddings y el mecanismo de atención? En este vídeo explico de forma sencilla y visual cómo funciona la inteligencia artificial generativa detrás de ChatGPT, Claude, Gemini, Grok, DeepSeek y otros modelos de lenguaje modernos. Aprenderás: ✅ Qué es un LLM (Large Language Model) ✅ Cómo aprende un modelo de IA ✅ Qué diferencia a ChatGPT, Gemini, Claude o DeepSeek ✅ Qué son los embeddings o representaciones vectoriales ✅ Cómo funciona el mecanismo de atención (Attention) ✅ Qué son los Transformers ✅ Cómo se procesa una pregunta desde que la escribes hasta que recibes una respuesta ✅ Por qué los LLM no memorizan respuestas, sino que predicen la siguiente palabra Este vídeo está pensado para principiantes, estudiantes, profesionales y cualquier persona que quiera entender cómo funciona realmente la inteligencia artificial sin necesidad de conocimientos técnicos previos. Capítulos: 00:00 Introducción 00:45 ¿Qué es un LLM? 03:15 Qué hace diferente a un modelo de IA 06:20 Capacidades de los LLM 07:45 Qué son los Embeddings 10:30 El mecanismo de Atención 13:20 Cómo funciona un LLM paso a paso 17:00 Conclusión Si te interesa la Inteligencia Artificial, los LLM, ChatGPT, los agentes de IA, RAG, automatización, Machine Learning y el futuro de la tecnología, suscríbete al canal. #ChatGPT #InteligenciaArtificial #LLM #Transformers #Embeddings #IA #MachineLearning #DeepLearning #Gemini #Claude #DeepSeek #GenerativeAI #AI

VTU ML BCS602 | Artificial Neuron Model & ANN Structure | Module 4 | Important

Welcome to Express VTU 4 All 🎓 In this video, we explain a very important Artificial Neural Network (ANN) theory question from Module–04: Neural Networks for VTU (BCS602). This question is frequently asked in VTU examinations and is a guaranteed 8–10 mark scoring question. 📌 Exact Question Covered Explain the simple model of an Artificial Neuron along with the Artificial Neural Network (ANN) structure. 🧠 What You Will Learn ✔ What is an Artificial Neuron ✔ Biological Neuron vs Artificial Neuron ✔ Components of Artificial Neuron ✔ Weighted Sum Calculation ✔ Activation Function ✔ Output Generation Process ✔ Structure of Artificial Neural Networks ✔ Input, Hidden, and Output Layers ✔ How to write answers in VTU exam format 📘 Introduction to Artificial Neuron An Artificial Neuron is the basic processing unit of an Artificial Neural Network (ANN). It receives input signals, processes them using weights and activation functions, and produces an output. Artificial neurons are inspired by the working of biological neurons in the human brain. 🧾 Simple Model of Artificial Neuron An artificial neuron consists of: 👉 1. Inputs (x₁, x₂, x₃, ...) Input values received from data. 👉 2. Weights (w₁, w₂, w₃, ...) Each input is assigned a weight representing its importance. 👉 3. Summation Unit Calculates weighted sum: z= i=1 ∑ n ​ w i ​ x i ​ +b 👉 4. Bias (b) Additional parameter used to improve learning capability. 👉 5. Activation Function Converts weighted sum into output. Examples: Sigmoid ReLU Tanh 👉 6. Output (y) Final result produced by the neuron. 🌟 Artificial Neuron Diagram x1 ──(w1)──┐ x2 ──(w2)──┼──► Σ + b ──► Activation Function ──► Output y x3 ──(w3)──┘ 📘 Artificial Neural Network (ANN) Structure An Artificial Neural Network consists of interconnected neurons arranged in layers. 👉 1. Input Layer Receives input data No computation performed Example: Age, Salary, Marks 👉 2. Hidden Layer(s) Performs computations Extracts patterns and features Example: Feature learning 👉 3. Output Layer Produces final prediction Example: Pass/Fail, Yes/No 🌟 ANN Structure Diagram Input Layer Hidden Layer Output Layer x1 ● ─────┐ ├──► ● ───┐ x2 ● ─────┤ ├──► ● (Output) ├──► ● ───┘ x3 ● ─────┘ 🎯 Working of ANN Step 1: Input data enters the network. Step 2: Weights are applied to inputs. Step 3: Weighted sum is computed. Step 4: Activation function processes the result. Step 5: Output is generated. Step 6: Weights are adjusted during training. ✅ Advantages of ANN ✔ Learns complex patterns ✔ Handles nonlinear data ✔ High prediction accuracy ✔ Self-learning capability 🎯 Exam Writing Strategy (VERY IMPORTANT) ✔ Define Artificial Neuron first ✔ Draw neuron diagram neatly ✔ Explain each component separately ✔ Draw ANN structure diagram ✔ Explain Input, Hidden, and Output layers 👉 This ensures full 10 marks 📊 Marks Weightage ✅ Usually asked for 8–10 Marks ✅ Theory + Diagram question ✅ Frequently repeated in VTU exams 🚀 Why This Question Is Important ✔ Foundation of Deep Learning ✔ Core concept of Neural Networks ✔ Frequently asked in VTU exams ✔ Important for AI and ML interviews 📚 Subject Details 📌 Subject: Machine Learning 📌 Subject Code: BCS602 📌 Module: 04 – Artificial Neural Networks 📌 University: VTU (CBCS Scheme) 📲 Free Notes & Updates Join Telegram for ML notes + important questions 👇 🔗 https://t.me/vtu4all artificial neuron model VTU artificial neural network structure ANN explained BCS602 machine learning module 4 neural networks VTU ML important questions #VTU #BCS602 #MachineLearning #ArtificialNeuron #ANN #NeuralNetworks #DeepLearning #VTUExams 👉 Watch till the end to master Artificial Neural Networks and learn how to draw ANN diagrams perfectly for full marks in VTU exams.

La verdad sobre la IA: Lo que necesitas saber (Explicación para principiantes)

¿Qué es realmente la Inteligencia Artificial? Más allá del ruido mediático y la ciencia ficción, la IA es la herramienta tecnológica más transformadora de nuestra era. En este video desglosamos el concepto desde cero, de forma clara, precisa y con un enfoque estrictamente profesional, sin tecnicismos innecesarios. Aprenderás la diferencia exacta entre Machine Learning y Deep Learning, cómo funcionan las redes neuronales y qué es la IA Generativa, todo explicado de forma detallada y estructurada para que domines los conceptos esenciales de inmediato. También analizamos los límites técnicos actuales, como los sesgos de datos y las llamadas "alucinaciones", para que comprendas el panorama real con criterio propio. Si quieres dominar los fundamentos de la tecnología que está redefiniendo la productividad y el futuro del trabajo, este video es el punto de partida que necesitas. #inteligenciaartificial #ia #gemini #claude #chatgpt #explicaciondefinitiva #guiadefinitivo

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

MNIST: clasifica imágenes con Python desde 0 | Curso ML 2026 #18

🔴 Hoy en directo: tu modelo dice 95% de accuracy... pero está fallando. Te muestro por qué y cómo detectarlo. ⏰ Inicio: martes 26 de mayo · 8:00 PM (GMT-5 / Colombia) 🎯 Qué vas a aprender: - Cargar y explorar el dataset MNIST con scikit-learn - Entrenar tu primer clasificador binario con SGDClassifier - Por qué el accuracy te miente en datasets desbalanceados - Leer e interpretar una matriz de confusión como un profesional - Precisión vs Recall: cuándo importa cada uno y cómo elegir 💎 Hazte miembro del canal para acceder a los lives editados sin pausas y al curso completo: 👉 https://www.youtube.com/channel/UCpqqJGMaVEmyinn1J-DhnYg/join 📂 Recursos del live: - Leer del capitulo 3 del libro Hands-On Machine Learning with Scikit-Learn and PyTorch - Aurélien Géron, lassiguientes secciones: MNIST, Training a Binary Classifier, Performance Measures, Measuring Accuracy Using Cross-Validation and Confusion Matrices. - Documentación scikit-learn: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html - Dataset MNIST en OpenML: https://www.openml.org/d/554 🔗 Sígueme también en: 💬 Discord 👉 https://discord.gg/NESrnqfNWF 📸 Instagram 👉 https://www.instagram.com/pildoras_de_programacion/ 🎵 TikTok 👉 https://www.tiktok.com/@pil_programacion 📘 Facebook 👉 https://www.facebook.com/pilprogramacion 📺 YouTube 👉 https://www.youtube.com/@pildorasdeprogramacion 🐳 ¡Nos vemos! #machinelearning #python #endirecto #mnist #scikitlearn #clasificacion #matrizdeconfusion #programacion