Videos de Educación
Videos educativos y de formación.
Educación 184 videos
Stanford que Ensina Mais sobre LLMs do que a Maioria dos Profissionais de IA
#llm #stanford #chatgpt #inteligenciaartificial #ia Em vez de assistir a uma hora de Netflix, assista a esta palestra de 2 horas da Stanford que vai te ensinar mais sobre como LLMs como ChatGPT e Claude são construídos do que a maioria das pessoas trabalhando em empresas de IA de ponta aprende em suas carreiras inteiras.Essa é uma das aulas mais valiosas que você vai encontrar sobre o funcionamento real dos grandes modelos de linguagem. Direto, profundo e sem enrolação.Se você quer entender de verdade como essas tecnologias são feitas por dentro, essa palestra é ouro puro. Pare o que está fazendo e invista essas 2 horas. Vale muito mais do que parece.Comente: você prefere usar IA ou entender como ela realmente funciona?
Autoencoders - Explained
An autoencoder is a neural network that learns to compress its own input and rebuild it. We start with the impossible-sounding task — squeeze a 49-pixel image down to two numbers and expand it back — then build the hourglass architecture, split it into encoder and decoder, and derive the reconstruction loss that trains the whole thing without a single label. By the end you'll see why the bottleneck is the real trick: forcing the network to carry meaning through a narrow waist is what organises the latent space and turns one idea into denoising, anomaly detection, and generation. *Related Videos* ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Variational Autoencoder - Explained: https://youtu.be/geH5HnRapRs Generative Adversarial Networks (GANs) - Explained: https://youtu.be/G-fXV-o9QV8 Convolutional Neural Networks (CNNs) - Explained: https://youtu.be/YGILT182T6w Recurrent Neural Networks (RNNs) - Explained: https://youtu.be/8G1fImBCMcQ Backpropagation is Just the Chain Rule: https://youtu.be/VCGlYxGJZ04 Activation Functions in Neural Networks - Explained: https://youtu.be/slp222E_0d4 UMAP - Explained: https://youtu.be/kwILqPNZyeo Normalization vs Standardization - Explained: https://youtu.be/87C5hkTY8RI *Contents* ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 00:00 - The Impossible Shortcut 00:22 - The Bottleneck 00:53 - Encoder and Decoder 01:21 - Reconstruction Loss 01:48 - The Latent Space 02:08 - Why This Matters *Follow Me* ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 🐦 X: @datamlistic https://x.com/datamlistic 📸 Instagram: @datamlistic https://www.instagram.com/datamlistic 📱 TikTok: @datamlistic https://www.tiktok.com/@datamlistic 👔 Linkedin: https://www.linkedin.com/company/datamlistic *Channel Support* ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ The best way to support the channel is to share the content. ;) If you'd like to also support the channel financially, donating the price of a coffee is always warmly welcomed! (completely optional and voluntary) ► Patreon: https://www.patreon.com/datamlistic ► Bitcoin (BTC): 3C6Pkzyb5CjAUYrJxmpCaaNPVRgRVxxyTq ► Ethereum (ETH): 0x9Ac4eB94386C3e02b96599C05B7a8C71773c9281 ► Cardano (ADA): addr1v95rfxlslfzkvd8sr3exkh7st4qmgj4ywf5zcaxgqgdyunsj5juw5 ► Tether (USDT): 0xeC261d9b2EE4B6997a6a424067af165BAA4afE1a #autoencoders #deeplearning #machinelearning
3. Deep Learning Explained | Applications, Challenges & Ethics
Dive deep into the world of Deep Learning Models in this comprehensive video covering Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Diffusion Models, and Transformers. Whether you're a student, AI enthusiast, or professional, this video breaks down complex concepts into easy-to-understand explanations. We begin with an introduction to deep learning, the driving force behind modern AI systems like ChatGPT and Google Gemini. Then, we explore how VAEs learn compressed probabilistic representations, how GANs generate realistic data through adversarial training, and how Diffusion Models create high-quality images by reversing noise. Next, we uncover the power of Transformers, the revolutionary architecture behind today’s most advanced natural language processing and multimodal systems. From text generation and translation to image synthesis, these models are transforming industries worldwide. 🚀 What you'll learn in this video: What deep learning models are and why they matter How VAEs, GANs, Diffusion Models, and Transformers work Real-world applications in healthcare, finance, entertainment, and robotics Key challenges like computational cost, bias, and interpretability Ethical concerns including misinformation, privacy, and responsible AI 🌍 Real-World Impact: Discover how deep learning powers innovations such as disease detection, fraud prevention, recommendation systems, autonomous vehicles, and more. ⚠️ Challenges & Ethics: We also discuss the limitations and ethical implications of AI, including bias in datasets, deepfakes, and the importance of transparent AI systems. 📌 Disclaimer: This video is created for educational and knowledge purposes only. The content is AI-generated, and while efforts have been made to ensure accuracy, some information may be incorrect or outdated. Viewers are encouraged to verify facts independently. #DeepLearning #ArtificialIntelligence #MachineLearning #Transformers #GANs #VAEs #DiffusionModels #AI #DataScience #NeuralNetworks #TechEducation #FutureOfAI #AIEthics #GenerativeAI deep learning models, VAE explained, GAN tutorial, diffusion models AI, transformers neural networks, generative AI explained, artificial intelligence basics, machine learning models, GPT and BERT explained, deep learning applications, AI in healthcare, AI ethics issues, neural networks tutorial, AI technologies 2026, generative models comparison
LLM Fundamentals Explained | How ChatGPT & Large Language Models Work”
🚀 Want to understand how ChatGPT and AI actually work? In this video, we break down Large Language Models (LLMs) in a simple and beginner-friendly way! Learn the fundamentals of LLMs, Tokens, Embeddings, Hidden States, AI Reasoning, and Generative AI with easy explanations and real-world examples. This video is perfect for students, beginners, M.Tech learners, AI enthusiasts, engineering students, interview preparation, and anyone curious about Artificial Intelligence. 📌 In this video, you will learn: ✅ What is a Large Language Model (LLM)? ✅ How LLMs work step-by-step ✅ Evolution of AI language understanding ✅ Tokens, Embeddings & Hidden States explained ✅ Difference between traditional NLP and Generative AI ✅ How ChatGPT understands language ✅ Real-world applications of LLMs ✅ AI concepts explained in simple language 🎯 Who should watch this? ✔ Engineering Students ✔ M.Tech / B.Tech Students ✔ AI & Machine Learning Beginners ✔ Cybersecurity & IT Students ✔ Interview Preparation Candidates ✔ Tech Enthusiasts 🔥 If you found this video useful, Like, Share & Subscribe for more AI, Cybersecurity, Python, Software Testing, and Technology videos! #LLM #ArtificialIntelligence #ChatGPT #MachineLearning #AIForBeginners #DeepLearning #NLP #LLMExplained #AITutorial #GenerativeAI
Como o "cérebro" da IA funciona? #inteligenciaartificial #chatgpt #openai
Como a IA moderna funciona? Uma rede neural artificial é um modelo matemático inspirado no funcionamento do cérebro humano. Essa é uma das bases tecnológicas de sistemas modernos de IA, incluindo LLMs como o ChatGPT. #largelanguagemodels #ia #neuralnetworks
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
Every Type of AI Explained — LLM, Image, Voice, Video, Code, Recommendation
You know ChatGPT. But ChatGPT is just one of six totally different types of AI — and you're using all six right now without knowing it. Here are all six, in order. Chapters: 0:00 — Hook 0:15 — Language Models (ChatGPT, Claude, Gemini) 0:33 — Image Generators (Midjourney, DALL·E, Stable Diffusion) 0:55 — Speech Models (Whisper, ElevenLabs) 1:15 — Video Generators (Sora, Veo, Runway) 1:35 — Code Models (Copilot, Cursor, Claude Code) 1:55 — Recommendation AI (YouTube, Spotify, TikTok algorithms) 2:10 — Recap + the next leap The next leap in AI? Combining all six into agents that can talk, see, build, and decide for you. 📌 Subscribe — every video, another broken pattern. #AI #ChatGPT #Midjourney #Sora #Copilot #DeepLearning #MachineLearning #AIExplained #TechExplained #BrokenPatterns
BPTT in RNN Explained | Backpropagation Through Time Made Easy
BPTT in RNN Explained | Backpropagation Through Time Made Easy Title Ideas 1. BPTT in RNN Explained | Backpropagation Through Time Made Easy 2. Master BPTT in Recurrent Neural Networks (RNN) 3. BPTT Tutorial for Beginners | Deep Learning & RNN 4. How RNN Learns? BPTT Explained Step by Step 5. Backpropagation Through Time (BPTT) in RNN | AI & Deep Learning 6. RNN Training Explained with BPTT | Vanishing Gradient Problem 7. BPTT in Deep Learning | Complete RNN Training Guide YouTube Description Welcome to another AI & Deep Learning tutorial on my channel Kanee Lifestyle and Vlogs! In this video, you will learn Backpropagation Through Time (BPTT) in Recurrent Neural Networks (RNNs) in a simple and easy-to-understand way. 📌 Topics Covered: What is RNN? Why BPTT is needed How Backpropagation Through Time works Unfolding RNN in time Gradient calculation in RNN Vanishing Gradient Problem Real-time Deep Learning examples BPTT explained step by step for beginners This video is perfect for: ✅ M.Tech IT Students ✅ AI & Machine Learning Learners ✅ Deep Learning Beginners ✅ Interview Preparation ✅ University Exam Preparation If you enjoy AI, Cybersecurity, Python, and Deep Learning content, subscribe and support the channel ❤️ Keywords / Tags BPTT, Backpropagation Through Time, RNN, Recurrent Neural Network, Deep Learning, Artificial Intelligence, Machine Learning, RNN Tutorial, BPTT Explained, Vanishing Gradient Problem, Neural Networks, LSTM, GRU, AI Tutorial, Deep Learning Tutorial, Python AI, AI for Beginners, MTech IT, AI Concepts, Sequence Models, NLP, TensorFlow, PyTorch, Deep Learning Interview Questions, RNN Training, Kanee Lifestyle and Vlogs
Why Modern LLMs Use GQA | Multi Query and Grouped Query Attention Visually Explained
Why do modern LLMs like Llama, Qwen, Gemma and Gemini use Grouped-Query Attention (GQA) instead of standard Multi-Head Attention (MHA)? In this video we build a complete intuition for Multi-Query Attention (MQA) and Grouped-Query Attention (GQA), two important transformer attention optimizations used in modern large language models. We understand how KV cache memory and memory bandwidth become major bottlenecks during autoregressive decoding and LLM inference, and why transformer architectures moved toward MQA and GQA attention mechanisms for faster inference and reduced KV cache size. We visually explain Multi-Query Attention, Grouped-Query Attention and the spectrum between MHA, MQA and GQA, including how shared key and value projections work across attention heads. We also compare MQA vs GQA performance, KV cache memory consumption, decoding latency and inference efficiency. The second half of the video focuses on implementation in PyTorch, where we start from a baseline Multi-Head Attention implementation and modify it step-by-step into Multi-Query Attention and Grouped-Query Attention. At the end of the video we go through GQA uptraining techniques proposed for converting existing transformer multi head checkpoints into MQA/GQA models. ⏱️ Timestamps: 00:00 Intro - KV Cache Memory & Bandwidth Bottleneck 02:46 Multi-Query Attention (MQA) Explained 03:57 Intuition for what MQA attention heads learn 06:18 Spectrum of MHA, MQA and GQA 07:48 Grouped-Query Attention (GQA) Explained 09:54 KV Cache Size Comparisons 11:04 MQA vs GQA vs MHA Performance Comparisons 11:51 Baseline Multi-Head Attention (MHA) Implementation 13:40 MQA Implementation in PyTorch 15:51 GQA Implementation in PyTorch 17:54 Uptraining MQA/GQA Models 📖 Resources: MQA Paper - https://arxiv.org/pdf/1911.02150 GQA Paper - https://arxiv.org/pdf/2305.13245 🔔 Subscribe: https://tinyurl.com/exai-channel-link Email - explainingai.official@gmail.com
GeoAI Tutorial: What is a Perceptron? | Basics of Neural Networks & AI
Learn the fundamentals of the Perceptron, the basic building block of Artificial Neural Networks (ANN), in this beginner-friendly GeoAI tutorial. 🧠🌍🛰️ In this video, you will understand how a perceptron works, how it processes input data, and why it is important in Machine Learning, Deep Learning, Computer Vision, and Geospatial AI (GeoAI). We will explain concepts such as weights, bias, activation functions, and decision boundaries using simple examples related to geospatial and remote sensing applications. 🔥 What You Will Learn ✔ What is a Perceptron? ✔ History of the Perceptron model ✔ Inputs, weights, and bias explained ✔ Activation functions and output generation ✔ How perceptrons make decisions ✔ Perceptron vs Artificial Neural Network (ANN) ✔ Applications in GeoAI and Remote Sensing 🌍 Why Perceptrons are Important? Perceptrons are the foundation of: Artificial Neural Networks (ANN) Deep Learning models Computer Vision systems Satellite image classification Intelligent geospatial analysis Understanding perceptrons helps build a strong foundation in AI and GeoAI. 🎯 Who Should Watch? Beginners in AI & Machine Learning GIS & Remote Sensing students GeoAI learners Computer Vision enthusiasts Data scientists and researchers 📌 SEO Keywords what is perceptron, perceptron tutorial for beginners, GeoAI perceptron explained, artificial neural network basics, machine learning perceptron model, AI fundamentals tutorial. 🔥 SEO Tags what is perceptron,perceptron tutorial for beginners,GeoAI perceptron explained,artificial neural network basics,machine learning perceptron model,AI fundamentals tutorial,deep learning basics,computer vision AI tutorial,GeoAI neural networks,Earth observation AI 🔥 High-Reach Hashtags #Perceptron #ArtificialIntelligence #MachineLearning #DeepLearning #GeoAI #ComputerVision #ANN #RemoteSensing #GIS #AI
How to Dominate AI SEO & LLM Rankings for More Leads in 2026
🚀 How to Dominate AI SEO & LLM Rankings for More Leads in 2026 AI search is changing everything. In this video, I break down how businesses can rank higher in ChatGPT, Google AI Overviews, Gemini, Claude, and other large language models to generate more leads and dominate search visibility in 2026. You’ll learn: ✅ How AI SEO actually works ✅ How LLMs choose which businesses to mention ✅ Website optimization strategies for AI search ✅ Entity SEO & topical authority tactics ✅ How to get more local leads from AI-driven search ✅ The future of SEO beyond traditional Google rankings If you want your business showing up in AI answers before your competitors, this video is for you. 📞 215-607-6482 📧 sean@needmomentum.com #AISEO #LLMSEO #ChatGPTSEO #SEO2026 #GoogleAI #DigitalMarketing #LeadGeneration #LocalSEO #AIOptimization #MarketingAgency
AI Evals Explained in 3 Steps 🤯 | How Top AI Companies Test Intelligence
Building an AI model is easy now… But proving that it actually works reliably? That’s the real challenge. In this BazAI breakdown, we explore how modern AI evaluation systems work using a simple 3-step framework. This video covers: ✅ Picking the Right AI Task ✅ Collecting Evaluation Datasets ✅ Developing AI Graders We explain how AI companies evaluate: LLMs, RAG systems, coding agents, autonomous AI workflows, reasoning models, safety systems, and multi-agent architectures. You’ll also learn about: 🔹 LLM-as-a-Judge systems 🔹 Human evaluation pipelines 🔹 Code-based grading 🔹 Benchmark datasets 🔹 AI safety testing 🔹 Agent evaluation frameworks As AI becomes more autonomous, evaluation is becoming more important than model size itself. The future of AI belongs to systems that are measurable, reliable, and trustworthy in real-world environments. Subscribe to BazAI for deep AI engineering breakdowns, autonomous agent systems, multimodal AI, and future technology explained simply.