Videos de Educación
Videos educativos y de formación.
Educación 184 videos
Spring AI 2.0: Custom Advisors for Tool & Token Logging
Ever wondered which tools your LLM is actually using and how many tokens each call is consuming? Spring AI Advisors give you AOP-like superpowers for your AI interactions, letting you intercept, log, and monitor every call to your language model. In this video, we explore Spring AI's Advisor API by building two custom advisors from scratch. The first is an AvailableToolsLoggingAdvisor that shows which tools are visible to the model before a call and which ones were actually invoked after. The second is a TokenCounterAdvisor that tracks prompt tokens, completion tokens, and total tokens with running totals across multiple calls. Along the way, you'll learn how advisors work as before/after interceptors for LLM calls, how to use the ChatResponse metadata, and how to wire tools into your Spring AI chat client. - Understand what Spring AI Advisors are and how they act as AOP for LLM calls - Build an AvailableToolsLoggingAdvisor to log which tools are loaded and which are invoked - Build a TokenCounterAdvisor to track prompt, completion, and total token usage per call - Learn how to use ChatResponse metadata for usage statistics - Wire up custom tools (like a DateTime tool) and see them in action with advisors 0:00 - Intro - Why I needed custom advisors 1:10 - What are Spring AI Advisors? 2:30 - Spring AI Reference Docs & Built-in Advisors 3:30 - Project setup on start.spring.io 4:30 - Configuring API key and model 5:15 - Creating the ChatController and ChatResponse 6:45 - Understanding token usage from ChatResponse 8:00 - Adding a DateTime tool for LLM calls 10:00 - Building the AvailableToolsLoggingAdvisor (before) 13:30 - Adding the after method to log invoked tools 16:30 - Testing the AvailableToolsLoggingAdvisor 17:45 - Building the TokenCounterAdvisor 20:30 - Testing the TokenCounterAdvisor 21:30 - Wrap up & what's next (Tool Searching) 🔗Resources & Links mentioned in this video: Spring AI Reference Documentation - Advisors: https://docs.spring.io/spring-ai/reference/api/advisors.html Spring Initializr: https://start.spring.io Dan Vega's Spring AI Workshop (GitHub): https://github.com/danvega/spring-ai-workshop 👋🏻Connect with me: Website: https://www.danvega.dev Twitter: https://twitter.com/therealdanvega Github: https://github.com/danvega LinkedIn: https://www.linkedin.com/in/danvega Newsletter: https://www.danvega.dev/newsletter SUBSCRIBE TO MY CHANNEL: http://bit.ly/2re4GH0 ❤️
What is a Neural Network? | Neural Network Explained for Beginners | @quicklearnerss
🧠 Neural Networks are the foundation of modern Artificial Intelligence, Machine Learning, and Deep Learning. In this beginner-friendly tutorial, you'll learn how Artificial Neural Networks (ANN) work using simple language, real-life examples, and easy-to-understand animations. Whether you're a Computer Science student, engineering student, AI enthusiast, or preparing for placements and interviews, this video will help you understand Neural Networks from scratch. 📌 In this video, you'll learn: ✔ What is a Neural Network? ✔ Why Neural Networks are important? ✔ Biological Neuron vs Artificial Neuron ✔ Structure of an Artificial Neural Network ✔ Input Layer, Hidden Layer & Output Layer ✔ Weights, Bias, and Activation Function ✔ Feed Forward Process ✔ Training a Neural Network ✔ Backpropagation (Basic Introduction) ✔ Real-life Applications of Neural Networks 🎯 This video is perfect for: • B.Tech / BCA / MCA Students • AI & Machine Learning Beginners • Deep Learning Beginners • Placement Preparation • University Exam Preparation • GATE Aspirants • Anyone curious about Artificial Intelligence ━━━━━━━━━━━━━━━━━━━━ 📚 Prerequisites: Basic understanding of mathematics is helpful but not required. ━━━━━━━━━━━━━━━━━━━━ 🔥 Related Videos: ▶ Artificial Intelligence Complete Playlist ▶ Machine Learning for Beginners ▶ Deep Learning Tutorial ▶ Perceptron Explained ▶ Activation Functions Explained ▶ Machine Learning Roadmap ━━━━━━━━━━━━━━━━━━━━ 💻 Technologies Discussed: Artificial Intelligence Machine Learning Deep Learning Artificial Neural Networks Perceptron Activation Functions Backpropagation ━━━━━━━━━━━━━━━━━━━━ 👍 If you found this video helpful: ✔ Like the video ✔ Share it with your friends ✔ Subscribe for more AI and Computer Science tutorials ✔ Turn on the notification bell 🔔 #NeuralNetwork #ArtificialIntelligence #MachineLearning #DeepLearning #AI #ANN #DataScience #ComputerScience #AIForBeginners #DeepLearningTutorial
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
The Perceptron: All of AI Is One Neuron
Try it yourself. The full written explainer and an interactive perceptron you can click are here: https://unrote.com/ai/the-perceptron/ The perceptron is a single artificial neuron, and it is the atom that every neural network is built from. In this from-scratch explainer we build one step by step: the inputs, the weights, the weighted sum, the bias, and the activation that makes it fire. Then we watch what one neuron can do, draw a straight line to separate data, meet the famous pattern it cannot solve (XOR), and see how stacking neurons into layers turns them into deep learning. What you will understand by the end: - What a neuron actually takes in, and why each input is a number - Weights: how much each piece of evidence counts - The weighted sum and the bias, in plain terms - Activation: the step function that outputs a one or a zero - A worked decision, start to finish - How a perceptron learns its weights - One straight line, and the hard limit of a single neuron - Why stacking neurons into layers gives you a neural network Understand it, don't memorize it.
CURSO COMPLETO DE CLAUDE COWORK (DO INICIANTE AO AVANÇADO)
Como usar corretamente o Claude Cowork Link: https://claude.com/product/cowork 👉Meu curso completo sobre Inteligências Artificiais com foco em renda extra e viver de internet: https://pay.kiwify.com.br/OUx0Hgb ✅Playlist de cursos gratuitos: https://youtube.com/playlist?list=PLNQBpbIienGEf4oI5z3wFOd_qexy0UrUe&si=oAqDO7cqeYPFB6X6 Editor: designersalvatore@gmail.com Neste canal, abordamos o que é a inteligência artificial e como ela está sendo aplicada em diferentes áreas, como tecnologia. Discutimos tipos de IA, como aprendizado profundo e racional, e os desafios e oportunidades que ela traz. O objetivo aqui é apresentar ferramentas de IA que facilitem o trabalho cotidiano das pessoas e como elas podem se preparar para o futuro com ela. Inscreva-se neste canal se quiser facilmente encontrar maneiras de trabalhar de forma independente, usando ferramentas como Midjourney, Stable diffusion, DALL·E 2, ChatGPT, etc. #midjourney #Stablediffusion #promptformidjourney #midjourneyai #chatgpt
Convexity Explained — Why Non-Convex Deep Nets Still Train ML Interview Question
Convex problems come with a guarantee; neural nets throw it away and train fine anyway. This video explains convexity, why local equals global, and why saddle points — not bad minima — are the real obstacle. In this video you'll learn: - The chord definition and the Hessian condition - Convex losses vs non-convex nets - The saddle-point surprise, and Jensen's inequality 🎯 Standalone episode from the AI/ML Engineering Interview series. 💬 Ever watched two seeds land in different minima? #MachineLearning #Optimization #Convexity #DeepLearning #MLInterview #InterviewPrep
Day 13: CNN Explained | 30 Days Free AI Engineering Bootcamp | Chitra Karanam
Welcome to Day 13 of the 30 Days AI Engineering Bootcamp! In today's live session, we'll dive deep into Convolutional Neural Networks (CNNs)—one of the most important deep learning architectures for Computer Vision. 📌 In this session, you'll learn: What is a Convolutional Neural Network (CNN)? Why CNNs are better than traditional Neural Networks for images Convolution Operation Filters (Kernels) and Feature Maps Stride and Padding Pooling Layers (Max & Average Pooling) Flatten Layer Fully Connected Layer CNN Architecture Explained Real-world Applications of CNN Interview Questions & Concepts Whether you're a beginner or preparing for AI/ML interviews, this session will help you build a strong foundation in Deep Learning. 🔥 This session is part of the 30 Days AI Engineering Bootcamp, where we're learning AI Engineering from scratch with hands-on examples. 👉 Don't forget to Like, Share, and Subscribe to Techonquer for daily AI Engineering sessions. #CNN #DeepLearning #ComputerVision #AIEngineer #ArtificialIntelligence #MachineLearning #NeuralNetworks #TensorFlow #PyTorch #Python #Techonquer #AIBootcamp #GenerativeAI #DataScience
As IAs generativas são máquinas de adivinhar?
Você já se perguntou por que ferramentas como ChatGPT, Gemini e Copilot conseguem produzir respostas tão convincentes? Será que elas realmente sabem ou apenas adivinham? Neste vídeo, explicamos de forma simples e objetiva como funcionam as IAs generativas por trás dos grandes modelos de linguagem (LLMs). Você vai entender por que esses sistemas são frequentemente descritos como máquinas de previsão, o que são os famosos tokens e por que uma IA pode gerar respostas impressionantes... e, ao mesmo tempo, cometer erros surpreendentes! Se você trabalha com tecnologia, dados, inteligência artificial ou simplesmente quer entender o que acontece por trás dessas ferramentas, este vídeo é para você. 📚 Fonte principal • Artigo da University of Central Florida: https://www.ucf.edu/artificial-intelligence/how-do-generative-ai-tools-like-chatgpt-work/ Capítulos: 00:00 - As IAs generativas parecem inteligentes... Não parecem? 00:32 - O artigo publicado pela University of Central Florida. 01:27 - O que são "tokens"? 01:57 - O comentário do pato. 02:13 - Por que a IA generativa erra? 02:52 - Encerramento.
I Trained My Own Tiny GPT From Scratch — And Ran It Locally
Most people think training an LLM means thousands of GPUs, massive datasets, and millions of dollars. That is true if you want to train something like ChatGPT. But in this video, I show how I trained a small decoder-only GPT-style language model from scratch using the TinyStories dataset — and ran it locally. This is not a chatbot. This is not instruction tuning. This is the basic idea behind language models: given a sequence of tokens, predict the next token. In this video, we go through the complete pipeline: * Preparing the TinyStories dataset * Creating a decoder-only GPT-style model * Training the model locally * Testing and evaluating the model * Generating text from the trained model * Serving the model using FastAPI The goal is not to beat ChatGPT. The goal is to understand how LLM training works end to end and prove that small, task-specific language models can still be useful. If you are learning AI engineering, I highly recommend going through this process at least once. After this, LLMs will stop feeling like magic and start feeling like systems you can understand, modify, and build. Get the code: https://www.rajkapadia.com/resources/432e3750-0eee-4da5-80b0-0cadf84f918b 00:00 The Myth About Training LLMs 00:53 TinyStories Dataset & Next Token Prediction 01:30 Why Small LLMs Still Matter 02:20 Project Structure & Important Files 04:03 Setup Commands & Training Pipeline 05:49 Generating Text From the Trained Model 06:24 Serving the Model With FastAPI 🚀 Join My Free Community! 👇 🌐 Nas.io - [Learn Everything About Chatbots](https://nas.io/learn-everything-about-chatbots) 📚 Master Google Dialogflow & Build Smart Chatbots! ES: [Enroll Now](https://www.udemy.com/course/master-google-dialogflow-build-smart-chatbots/) CX: [Enroll Now](https://www.udemy.com/course/master-dialogflow-cx-build-engaging-chatbots-2025) 💬 Join Our Discord Group & Connect with Like-Minded People! 🔗 [Discord Community](https://discord.gg/dKruft7Kqs) 🔥 Get Exclusive Perks & Behind-the-Scenes Content! 🎥 [Join This Channel](https://www.youtube.com/channel/UCOT01XvBSj12xQsANtTeAcQ/join) 💡 Need a Custom Chatbot or AI/ML/DL Solution? 📩 Contact me for: 🤖 Chatbot Development | 🧠 AI/ML/DL Projects 🎯 Hire Me on Freelance Platforms! 💼 [Fiverr Profile](https://www.fiverr.com/rajkkapadia) 💼 [Upwork Profile](https://www.upwork.com/freelancers/~0176aeacfcff7f1fc2) 💼 [LinkedIn Profile](https://www.linkedin.com/in/rajkkapadia/) 📢 Share Your Thoughts! 💬 Drop a comment below & let me know what you think about this video! 📌 Don't Forget to: 👍 LIKE | 🔔 SUBSCRIBE | 💬 COMMENT 🎶 Enjoy Life, Feel the Music. ✌️ Peace.
Testei a melhor IA de Vídeos Automáticos para Viralizar na COPA
Como criar conteúdo viral para copa do mundo Link da plataforma: https://magiclight.ai/events/world-cup/?code=MLHYJPrA 👉Meu curso completo sobre Inteligências Artificiais com foco em renda extra e viver de internet: https://pay.kiwify.com.br/OUx0Hgb ✅Playlist de cursos gratuitos: https://youtube.com/playlist?list=PLNQBpbIienGEf4oI5z3wFOd_qexy0UrUe&si=oAqDO7cqeYPFB6X6 Editor: designersalvatore@gmail.com Neste canal, abordamos o que é a inteligência artificial e como ela está sendo aplicada em diferentes áreas, como tecnologia. Discutimos tipos de IA, como aprendizado profundo e racional, e os desafios e oportunidades que ela traz. O objetivo aqui é apresentar ferramentas de IA que facilitem o trabalho cotidiano das pessoas e como elas podem se preparar para o futuro com ela. Inscreva-se neste canal se quiser facilmente encontrar maneiras de trabalhar de forma independente, usando ferramentas como Midjourney, Stable diffusion, DALL·E 2, ChatGPT, etc. #midjourney #Stablediffusion #promptformidjourney #midjourneyai #chatgpt
Large Language Models (LLMs) Explained From Scratch (Complete Beginner's Guide) | TAB 47
#largelanguagemodels #llmfullcourse #llmfromscratch Want to understand Large Language Models (LLMs) without complicated math? In this video, you'll learn what a Large Language Model is, how LLMs work, and why models like ChatGPT, Claude, Gemini, and DeepSeek are transforming AI. This complete LLM tutorial for beginners explains every fundamental concept step by step, including language models, AI models, next-word prediction, pre-training, fine-tuning, parameters, weights, biases, datasets, compute, and the difference between Small Language Models (SLMs) and Large Language Models (LLMs). Whether you're preparing for AI Engineering, Machine Learning, Generative AI, Prompt Engineering, or simply want to understand how modern AI works, this video builds a strong foundation from scratch. Click to start your Career in GenAI - MICROSOFT GenAI Course - https://bit.ly/4oxRGaF only Rs 299/ Join our WhatsApp Channel to get the latest updates, learning resources, job trends, and exclusive content : https://whatsapp.com/channel/0029Vb7v6JA3LdQdLhL9rQ2i Below are the concepts covered in this video : 00:26 – Introduction & What You'll Learn 01:54 – What Is a Large Language Model (LLM)? 03:50 – What Is Language? (Vocabulary, Grammar & Meaning) 05:31 – Programming Languages vs Human Languages 06:45 – What Is an AI Model? 10:33 – Models as Mathematical Approximations 12:13 – What Is a Language Model? 12:48 – Next Word Prediction Explained 15:10 – Why Is It Called a Large Language Model? 17:27 – Datasets, Parameters & Compute 18:43 – Weights, Biases & Parameters 19:22 – Small Language Models (SLMs) vs Large Language Models (LLMs) 21:48 – LLM Recap: Language + Model + Large 22:24 – Sequence Prediction Example 26:44 – Pre-training vs Fine-tuning 28:57 – Discriminative vs Generative Models 29:41 – What Is a Generative Model? #generativeai #deeplearning #llm #softwareengineer #webdevelopment #localllm #aiagents #aitools #llmtutorial