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Reels en Tendencia

(12)
#Deeplearning Reel by @awomanindatascience - It's Day 14 of building a LLM from scratch ✨ Most people think LLMs are complex because of code. They're complex because of configuration and scale.
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@awomanindatascience
It’s Day 14 of building a LLM from scratch ✨ Most people think LLMs are complex because of code. They’re complex because of configuration and scale. Today I broke down the GPT-2 config that defines how the model thinks, remembers, and attends. GPT-2 is just a set of numbers that define scale: vocab size, context length, embedding dimension, layers, and attention heads. Breaking down the GPT-2 (124M) configuration: 50,257-token vocabulary, 1,024-token context, 768-dimensional embeddings, 12 transformer layers with 12 attention heads, dropout 0.1, and bias-free QKV projections. Understanding these parameters is key to scaling LLMs efficiently. #deeplearning #generativeai #womenwhocode #largelanguagemodels
#Deeplearning Reel by @code_helping - A neural network visualizer that shows how an MLP learns step by step. Runs in the browser, trained with PyTorch, and works best on desktop. . Source:
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@code_helping
A neural network visualizer that shows how an MLP learns step by step. Runs in the browser, trained with PyTorch, and works best on desktop. . Source: 🎥 DFinsterwalder (X) . . #coding #programming #softwaredevelopment #computerscience #cse #software #ai #ml #machinelearning #computer #neuralnetwork #mlp #ai #machinelearning #deeplearning #visualization #threejs #pytorch #webapp #tech
#Deeplearning Reel by @datasciencebrain (verified account) - 🎓 FREE Stanford AI & ML Courses You Can't Miss! Stanford just dropped 11 game-changing courses that'll take you from ML basics to cutting-edge LLMs
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@datasciencebrain
🎓 FREE Stanford AI & ML Courses You Can't Miss! Stanford just dropped 11 game-changing courses that'll take you from ML basics to cutting-edge LLMs - and they're all FREE! 🚀 Whether you're starting your AI journey or leveling up your skills, this is your roadmap: ✅ Machine Learning fundamentals ✅ Deep Learning & Computer Vision ✅ Reinforcement Learning ✅ NLP & Transformers ✅ Generative AI & LLMs ✅ Building Language Models from scratch No fluff. No gatekeeping. Just world-class education from Stanford's top professors. The best part? You can learn at your own pace and build a portfolio that stands out. Which course are you starting with? Drop a number 1-11 in the comments! 👇 ⚠️ ALL COURSES ARE ON YOUTUBE. JUST SEARCH WITH THE NAMES. Save this post and share it with someone leveling up their AI career in 2025! 💡 📲 Follow @datasciencebrain #datasciencebrain for Daily Notes 📝, Tips ⚙️ and Interview QA🏆 . . . . . . [datascienceroadmap, airoles, mlengineerpath, datasciencejobs, analyticscareer, datatechskills, mlopsengineer, dataengineerskills, aiindustrytrends, techlearningguide] #datascience #machinelearning #python #ai #dataanalytics #artificialintelligence #deeplearning #bigdata #agenticai #aiagents #statistics #dataanalysis #datavisualization #analytics #datascientist #neuralnetworks #100daysofcode #genai #llms #datasciencebootcamp
#Deeplearning Reel by @dev2esh - Follow for Ai/Robotics content Dm for link ⬇️⬇️⬇️⬇️ Beginner Level Python & ML Foundations https://www.youtube.com/playlist?list=PLPTV0NXA_ZSgMaz0
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@dev2esh
Follow for Ai/Robotics content Dm for link ⬇️⬇️⬇️⬇️ Beginner Level Python & ML Foundations https://www.youtube.com/playlist?list=PLPTV0NXA_ZSgMaz0Mu-SjCPZNUjz6-6tN Mathematics for Machine Learning https://www.youtube.com/playlist?list=PLPTV0NXA_ZSiR4_XoR1wy-3bv6J0oZ9Zs Machine Learning Fundamentals https://www.youtube.com/playlist?list=PLMrJAkhIeNNR3sNYvfgiKgcStwuPSts9V Deep Learning Basics https://www.youtube.com/playlist?list=PLMrJAkhIeNNT14qn1c5qdL29A1UaHamjx Introduction to Robotics (Conceptual) https://www.youtube.com/watch?v=FGnAeUXRZ4E Robot Kinematics & Motion (Beginner-friendly) https://www.youtube.com/@ArticulatedRobotics ROS & Robotics Fundamentals https://www.youtube.com/playlist?list=PLLSegLrePWgJudpPUof4-nVFHGkB62Izy Intermediate Level Machine Learning (Reinforcement & Applied ML) https://www.youtube.com/playlist?list=PLPTV0NXA_ZSgMaz0Mu-SjCPZNUjz6-6tN Reading & Understanding AI Research Papers https://www.youtube.com/@aipapersacademy/videos Applied Deep Learning & Vision https://www.youtube.com/playlist?list=PLMrJAkhIeNNQe1JXNvaFvURxGY4gE9k74 Practical Robotics Engineering https://www.youtube.com/@kevinwoodrobotics Neural Networks from First Principles https://www.youtube.com/@AndrejKarpathy Advanced Level Advanced Robotics & Control Systems https://www.youtube.com/playlist?list=PLMrJAkhIeNNR20Mz-VpzgfQs5zrYi085m Deep Learning & AI Systems (Stanford-level) https://www.youtube.com/playlist?list=PLoROMvodv4rNiJRchCzutFw5ItR_Z27CM Reinforcement Learning & Advanced ML https://www.youtube.com/playlist?list=PLZnJoM76RM6IAJfMXd1PgGNXn3dxhkVgI #learnings #ML #education #study #engineering
#Deeplearning Reel by @insightforge.ai - This is a live demonstration of a convolutional neural network (CNN) recognizing handwritten digits in real time. In the video, a person writes numbe
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@insightforge.ai
This is a live demonstration of a convolutional neural network (CNN) recognizing handwritten digits in real time. In the video, a person writes numbers on a touchscreen tablet while the connected system processes the image step by step. Viewers can watch the digit flow through different CNN layers, visualized as animated tensors, showing how features are extracted and transformed. By the end, the model correctly identifies the handwritten number, giving a clear, intuitive look at how CNNs perform classification behind the scenes. C: okdalto #cnn #machinelearning #deeplearning #computervision #datascience
#Deeplearning Reel by @dailymathvisuals - The Kernel Trick explained in 75 seconds ✨ Ever wondered how machine learning separates data that seems impossible to separate? Here's the secret:
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@dailymathvisuals
The Kernel Trick explained in 75 seconds ✨ Ever wondered how machine learning separates data that seems impossible to separate? Here's the secret: → In 2D, no line can separate this data → But lift it into 3D... → A simple plane does the job perfectly This is why Support Vector Machines are so powerful 🧠 Save this for later 🔖 — Follow @dailymathvisuals for daily ML & math visualizations #machinelearning #artificialintelligence #datascience #python #coding #svm #kerneltrick #ai #tech #programming #learnwithreels #educationalreels #mathvisualization #deeplearning #engineering
#Deeplearning Reel by @vision_nests - the work of Japanese visual artist Kensuke Koike, who uses a "no more, no less" philosophy to deconstruct vintage photographs into new, often surreal
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@vision_nests
the work of Japanese visual artist Kensuke Koike, who uses a "no more, no less" philosophy to deconstruct vintage photographs into new, often surreal forms. By passing a photograph of a dog through a pasta machine, Koike creates a physical representation of how a Convolutional Neural Network (CNN) processes visual data. In computer science, this serves as a metaphor for the Convolutional Layer, where "filters" scan an image to break it down into smaller, manageable pieces of data. Just as the pasta machine slices the image into uniform strips, a CNN extracts "features"—like edges, curves, and textures—rather than trying to understand the entire complex image all at once. ​Once the image is shredded, the artist rearranges the strips into a grid, which mirrors the Pooling or Downsampling stage of a neural network. This process reduces the spatial size of the data to decrease the computational power required while preserving the most critical information. The resulting "pixelated" and repetitive dogs seen at the end of the clip represent the Feature Maps that deep learning models use to identify patterns. By the final frame, the network (or the viewer) can recognize the "dogness" of the image through these simplified, reconstructed blocks, perfectly illustrating the journey from raw pixels to high-level object recognition. Interested in? Follow:-@vision_nests #science #pixilated @vision_nests
#Deeplearning Reel by @startalk (verified account) - StarTalk: "Why don't we just unplug it?" The AI: 😇 link in bio 🔗to watch our fascinating episode on whether AI is hiding its full power, featuring
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@startalk
StarTalk: “Why don’t we just unplug it?” The AI: 😇 link in bio 🔗to watch our fascinating episode on whether AI is hiding its full power, featuring computer scientist, Nobel Laureate, and one of the architects of AI, Geoffrey Hinton! #StarTalk #AI #DeepLearning
#Deeplearning Reel by @woman.engineer (verified account) - 🚀 How to Become an AI Engineer in 2026 👩🏻‍💻Save for later Step-by-step Roadmap 🔹 PHASE 1: Foundations (0-3 Months) Don't skip this. Weak foundat
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@woman.engineer
🚀 How to Become an AI Engineer in 2026 👩🏻‍💻Save for later Step-by-step Roadmap 🔹 PHASE 1: Foundations (0–3 Months) Don’t skip this. Weak foundations = stuck later. 1️⃣ Programming (Must-Have) Python: loops, functions, OOP Libraries: NumPy, Pandas, Matplotlib / Seaborn 📌 Practice daily: LeetCode (easy) HackerRank (Python) 2️⃣ Math for AI (Enough, not PhD level) Focus only on: Linear Algebra (vectors, matrices) Probability & Statistics Basic Calculus (idea of gradients) 📌 Conceptual understanding is enough — no heavy theory. 🔹 PHASE 2: Machine Learning (3–6 Months) Learn: Supervised & Unsupervised Learning Feature Engineering Model Evaluation Algorithms: Linear & Logistic Regression KNN Decision Trees Random Forest SVM K-Means Tools: Scikit-learn 📌 Project Ideas: House price prediction Student performance prediction Credit risk model 🔹 PHASE 3: Deep Learning & AI (6–10 Months) Learn: Neural Networks & Backpropagation CNN (Images) RNN / LSTM (Text) Transformers (Basics) Frameworks: TensorFlow or PyTorch (choose ONE) 📌 Project Ideas: Face mask detection Image classifier Spam email detector Basic chatbot 🔹 PHASE 4: Modern AI (2025–2026) 🔥 This is where the JOBS are coming from. Learn: Generative AI Large Language Models (LLMs) Prompt Engineering RAG (Retrieval-Augmented Generation) Fine-tuning models Tools: OpenAI API Hugging Face LangChain Vector Databases (FAISS / Pinecone) 📌 Project Ideas: AI PDF Chat App Resume Analyzer AI Study Assistant AI Customer Support Bot 🔹 PHASE 5: MLOps & Deployment (CRITICAL) Learn: Git & GitHub Docker (basics) FastAPI / Flask Cloud basics (AWS or GCP) Deploy: ML models as APIs AI apps on the cloud 📌 Recruiters LOVE deployed projects. . . . #datascientist #aiengineer #codinglife #softwaredeveloper #programming
#Deeplearning Reel by @wdf_ai - Building your own ChatGPT-like model at small scale is more achievable than you think. In Large Language Model lots of dataset and compute is requir
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@wdf_ai
Building your own ChatGPT-like model at small scale is more achievable than you think. In Large Language Model lots of dataset and compute is required but the core structure of transformers remains same. 3 free resources that actually work — LLM basics, build from scratch, full training pipeline with fine-tuning. Comment “LLM” and I’ll DM you all the links. #llm #gpt #machinelearning #deeplearning #aiforbeginners
#Deeplearning Reel by @amanrahangdale_2108 (verified account) - Read Here ⬇️ 1️⃣ AI & Machine Learning Why: AI is driving automation and smart decision-making Learn: Python, ML, Deep Learning, GenAI Roles: AI Eng
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@amanrahangdale_2108
Read Here ⬇️ 1️⃣ AI & Machine Learning Why: AI is driving automation and smart decision-making Learn: Python, ML, Deep Learning, GenAI Roles: AI Engineer, ML Engineer, Data Scientist 2️⃣ Data Science & Data Analytics Why: Businesses rely on data-driven decisions Learn: Python, SQL, Statistics, Visualization Roles: Data Analyst, Data Scientist 3️⃣ Cloud Computing & DevOps Why: Modern apps need scalability and reliability Learn: AWS/GCP, Docker, Kubernetes, CI/CD Roles: Cloud Engineer, DevOps Engineer 4️⃣ Cybersecurity Why: Cyber threats are increasing rapidly Learn: Network Security, Ethical Hacking, OWASP Roles: Security Analyst, Cybersecurity Engineer 5️⃣ Agentic AI Why: Next-gen AI that can plan and act autonomously Learn: LLMs, AI Agents, LangChain, Automation Roles: AI Engineer, Automation Engineer 6️⃣ Full-Stack Web Development + AI Integration Why: Companies need complete AI-powered products Learn: React, Node.js, Databases, AI APIs Roles: Full-Stack Developer, Product Engineer 💬 Comment the skill name for detailed learning roadmap & best resources 🚀 📱 Follow @amanrahangdale_2108 for more Free Courses, Tech Updates, and Career Tips every week 💡
#Deeplearning Reel by @studywithaffu (verified account) - 🌅 BEST TIME TO STUDY (Scientifically Proven!) 🕔 5AM - 8AM → PEAK FOCUS MODE ✨ Fresh mind 🔕 Zero distractions 🧠 Memory power at MAX 🕙 10AM - 2PM
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@studywithaffu
🌅 BEST TIME TO STUDY (Scientifically Proven!) 🕔 5AM – 8AM → PEAK FOCUS MODE ✨ Fresh mind 🔕 Zero distractions 🧠 Memory power at MAX 🕙 10AM – 2PM → DEEP LEARNING HOURS ⚡ Brain at highest alertness 📚 Best for tough subjects 💡 Perfect for new concepts 🌆 6PM – 9PM → REVISION & RECALL 🔁 Revise & reinforce 📝 Memory sticks better 🎯 Best for summaries & practice ⚡ Stop studying randomly. Start studying scientifically. 👉 Follow @studywithaffu for daily brain-boosting study hacks✨💫 . . . . #studygram #medicalstudent #motivationalreels #studytips #studytricks

✨ Guía de Descubrimiento #Deeplearning

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#Deeplearning es una de las tendencias más populares en Instagram ahora mismo. Con más de 5.6 million publicaciones en esta categoría, creadores como @studywithaffu, @wdf_ai and @dev2esh lideran con su contenido viral. Explora estos videos populares de forma anónima en Pictame.

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