Videos de neural networks
Videos etiquetados con "neural networks"
neural networks 7 videos
How to Choose the Best AI Image Generator (Midjourney vs DALL-E vs Stable Diffusion) (2026)
In this video: How to Choose the Best AI Image Generator (Midjourney vs DALL-E vs Stable Diffusion) (2026) Subscribe and hit the bell for new videos every week! 🔗 TOOLS & RESOURCES: • Claude AI (free): https://claude.ai • ChatGPT: https://chat.openai.com • Canva (free — join link): https://www.canva.com/join/myv-gzr-ptz • Notion (free): https://notion.so • Zapier (free tier): https://zapier.com • NordVPN (67% off): https://go.nordvpn.net/aff_c?offer_id=15&aff_id=823d06043aa7fa9cfa5d7c3185cc204b99c104c7412d135e41dcb31207afe3b7 • SafetyWing Insurance: https://safetywing.com/?referenceID=26525115&utm_source=26525115&utm_medium=Ambassador • Hostinger (web hosting): https://www.hostinger.com/it?REFERRALCODE=KZXALF199EZ9 📥 FREE DOWNLOAD — AI Tools Cheat Sheet 2026: https://scalabletools.gumroad.com/l/oruyf 🚀 Want AI to run your entire business? Try S.C.A.L.A.: https://get-scala.com/?utm_source=youtube&utm_medium=aitoollab&utm_campaign=video 📧 Sponsorships & collabs: sponsor@get-scala.com ━━━━━━━━━━━━━━━━━━━━━ 🔔 Subscribe for daily AI tool reviews! 👍 Like this video if it helped you! ━━━━━━━━━━━━━━━━━━━━━ #AITools #AI #Productivity #AI2026 #TechReview #productivity #tutorial #2026
Understanding Large Language Models (LLM) | Transformer Architecture, AI Concepts & Future Explained
Discover the complete world of Large Language Models (LLMs) in this detailed educational video designed for students, researchers, AI enthusiasts, educators, developers, and technology learners. In this in-depth session, we explore the latest concepts behind modern Artificial Intelligence systems including Transformer Architecture, Self-Attention Mechanisms, Tokenization, Embeddings, Neural Networks, Context Windows, Retrieval-Augmented Generation (RAG), AI Agents, Multimodal AI, and the future of Generative AI technologies. This educational video explains how modern AI systems process and understand human language using billions of parameters and large-scale datasets. Whether you are beginning your AI journey or already exploring Machine Learning and Natural Language Processing, this video provides a structured and easy-to-understand explanation of the most important concepts behind modern LLMs. The video also covers the evolution from traditional NLP systems to advanced Transformer-based architectures that power today’s AI assistants, intelligent chatbots, research systems, coding assistants, and enterprise AI applications. 📘 Topics Covered in This Video What are Large Language Models (LLMs)? Evolution of NLP and AI Language Systems Transformer Architecture Explained Self-Attention Mechanism Tokens and Tokenization Word Embeddings and Vector Representations Parameters and Neural Networks Training and Fine-Tuning of LLMs Context Windows and Long-Context AI Encoder vs Decoder Models Retrieval-Augmented Generation (RAG) Hallucinations and Limitations of AI AI Safety and Alignment Multimodal AI Systems AI Agents and Autonomous Workflows Latest Trends in Generative AI Real-World Applications of LLMs 🎯 Who Should Watch This Video? This video is highly useful for: Artificial Intelligence Students Machine Learning Enthusiasts NLP Researchers Engineering Students Data Science Learners AI Developers Educators and Teachers Research Scholars Technology Professionals Anyone curious about modern AI systems 🚀 Why This Video Matters Large Language Models are rapidly transforming industries including: Education Healthcare Cybersecurity Software Engineering Scientific Research Business Automation Digital Content Creation Understanding how LLMs work is becoming an essential skill in the modern AI-driven world. This video aims to simplify advanced AI concepts into a structured educational format suitable for learning, teaching, research, and knowledge-building purposes. 📌 Educational Disclaimer This video is created strictly for educational, learning, research, awareness, and knowledge-building purposes only. Some portions of this content are AI-generated and may contain inaccuracies, omissions, outdated information, or unintended errors. Viewers are strongly encouraged to independently verify facts, technical details, research findings, and practical implementations from official and trusted sources before applying them in academic, professional, technical, legal, medical, or commercial environments. This content does not promote misuse of AI technologies and is intended solely for responsible educational understanding. 🔔 Support the Channel If you found this educational AI content useful: Like the video Share with learners and researchers Subscribe for more AI, Machine Learning, NLP, and Technology educational content Enable notifications for future updates #LLM #ArtificialIntelligence #GenerativeAI #MachineLearning #NLP #Transformers #LargeLanguageModels #AI #DeepLearning #NeuralNetworks #AIExplained #DataScience #AI2026 #Technology #EducationalVideo #AIResearch #NotebookLM #FutureOfAI #SelfAttention #RAG Large Language Models, LLM tutorial, What are LLMs, Transformer Architecture, Generative AI, Artificial Intelligence, NLP tutorial, Self Attention Mechanism, AI explained, Machine Learning tutorial, Deep Learning, Neural Networks, AI Agents, Multimodal AI, Retrieval Augmented Generation, RAG systems, Tokenization, Embeddings, Context Window, AI education, NotebookLM content, AI concepts explained, latest AI trends 2026, educational AI video, language models tutorial, ChatGPT concepts, Transformer neural network, AI learning, AI research, Future of AI, AI for students, AI technology explained, Generative AI tutorial, modern AI systems, Large Language Model architecture, NLP concepts, AI knowledge video, educational technology content
How Neural Networks Actually Learn -- Backpropagation & Gradient Descent Explained Visually
Every AI system you've ever interacted with -- from image recognition on your phone to the large language models reshaping how we work -- was built on the same elegant loop. This video breaks down the complete neural network training process from first principles: how neurons compute weighted sums, why activation functions like ReLU and sigmoid are non-negotiable, how a loss function converts "being wrong" into a precise number, and how backpropagation uses the chain rule to flow that error signal backward through every layer. Intuition comes first. Equations come second. By the end, you won't just know the vocabulary of deep learning -- you'll understand why the math works. * Why do neural networks need activation functions at all? * What is a loss function and how does gradient descent minimize it? * How does backpropagation actually calculate gradients in deep networks? * What is the vanishing gradient problem and why did ReLU solve it? * How does the learning rate control the speed vs. stability of training? * What happened in 1986 that made modern deep learning possible? This video draws on Stanford CS231n, MIT lecture notes, Michael Nielsen's Neural Networks and Deep Learning, and the landmark 1986 paper by Rumelhart, Hinton & Williams -- not to show off sources, but because getting the math right matters. No hand-wavy metaphors that break down under scrutiny. No oversimplified cartoons. Just the actual mechanism, made as clear as it can be. Got a question the video didn't answer? Drop it in the comments -- we read them. #NeuralNetworks #DeepLearning #MachineLearning #AIExplained #Backpropagation #ai #anthropic #openai #microsoft
LLMs Explained in 20 Minutes | The Transformer Behind ChatGPT, Gemini & Claude
🚀 LLMs Explained in 10 Minutes | The Transformer Behind ChatGPT, Gemini & Claude Ever wondered how ChatGPT, Gemini, Claude, and other AI assistants actually work? In this video, we'll break down Large Language Models (LLMs) in the simplest way possible. You'll learn how AI evolved from traditional neural networks to the revolutionary Transformer Architecture, the breakthrough that powers modern AI. We'll also explore the Attention Mechanism, the core idea that allows LLMs to understand context, focus on important words, and generate human-like responses. What You'll Learn ✅ What is an LLM (Large Language Model)? ✅ Why RNNs struggled with long context ✅ How Transformer Architecture works ✅ What is the Attention Mechanism? ✅ Why Transformers changed AI forever ✅ How ChatGPT, Gemini, and Claude generate responses ✅ The foundation behind modern Generative AI Whether you're a student, developer, cloud engineer, AI enthusiast, or preparing for AI interviews, this video will help you understand the fundamentals of LLMs without complicated math. 🔥 If you enjoy AI, Generative AI, Agentic AI, Google Cloud, Gemini, MCP, and modern AI architectures, make sure to subscribe for more content. #LLM #ChatGPT #Gemini #Claude #Transformer #GenerativeAI #ArtificialIntelligence #MachineLearning #AIExplained #TechTrapture Playlists Google Agent Development Kit (ADK) https://www.youtube.com/playlist?list=PLLrA_pU9-Gz2HwepRUVpq1TEPuYWo_fSi Learn Airflow https://www.youtube.com/playlist?list=PLLrA_pU9-Gz3i8qw6yakrfJzx75W_vVaH Learn Google Cloud in 2025 https://youtube.com/playlist?list=PLLrA_pU9-Gz2OnBoICkewd9-Fc9Mi0nm7&si=8kkB3ct5wDHCMkoi Data Engineering Hands-on Projects https://www.youtube.com/playlist?list=PLLrA_pU9-Gz2DaQDcY5g9aYczmipBQ_Ek Looking to get in touch? Drop me a line at vishal.bulbule@techtrapture.com Linkedin https://www.linkedin.com/in/vishal-bulbule/ Medium Blog https://medium.com/@VishalBulbule Github Source Code https://github.com/vishal-bulbule
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
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
How AI Creates Images Explained | Diffusion Models & Prompt to Image AI
How does AI actually create images from a simple text prompt? In this video, we break down the magic behind AI image generation in 4 simple stages — from training the AI's visual brain to turning random noise into stunning, detailed pictures. Whether you're using Midjourney, DALL·E, Stable Diffusion, or any other AI art generator, this guide will help you understand exactly what's happening behind the scenes when you type a prompt and watch an image appear. 🔑 What you'll learn: • Stage 1: How the AI Learns — Building a Visual Brain with training data • Stage 2: How AI Analyzes Your Prompt — Turning words into meaning • Stage 3: From Noise to Image — The Diffusion Process explained • Stage 4: Why AI Generates Multiple Versions of the same prompt We also explore the bigger question: If an AI can create art, what does it mean to be an artist? Perfect for beginners, creators, designers, and anyone curious about how generative AI, machine learning, neural networks, and diffusion models work together to create images. 👍 Like, subscribe, and turn on notifications for more easy explainers on AI, technology, and the future of creativity. #AI #ArtificialIntelligence #AIArt #ImageGeneration #DiffusionModels #MachineLearning #GenerativeAI #DeepLearning #PromptEngineering #AIExplained #Midjourney #StableDiffusion #DALLE #TechExplained #FutureOfAI