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#Pytorch 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
#Pytorch Reel by @real_kingsleymayor - 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 #coding #
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@real_kingsleymayor
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 #coding #programming #softwaredevelopment #computerscience #cse
#Pytorch Reel by @codecademy (verified account) - PyTorch vs TensorFlow: What's the difference and which one should you learn? If you're diving into machine learning or building your first neural net
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@codecademy
PyTorch vs TensorFlow: What’s the difference and which one should you learn? If you’re diving into machine learning or building your first neural network, you’ve probably seen these names come up everywhere! The TL;DR is: 💡 PyTorch = intuitive, dynamic, and easier to learn 🚀 TensorFlow = scalable, versatile, and great for deployment But truthfully, you can’t go wrong with either! And we have beginner-friendly courses to help you get started with both. #PyTorch #TensorFlow #DeepLearningFrameworks #MachineLearning #AIFrameworks #NeuralNetworks #LearnMachineLearning #DataScience #AIforBeginners #CodingForAI #MLFrameworks
#Pytorch Reel by @learnbayofficial - Everyone wants to learn AI. Almost nobody learns the stack. A lot of developers jump straight to models. PyTorch. TensorFlow. Transformers. But rea
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@learnbayofficial
Everyone wants to learn AI. Almost nobody learns the stack. A lot of developers jump straight to models. PyTorch. TensorFlow. Transformers. But real AI systems rarely fail because of the model. They fail because everything around the model is missing. Data pipelines. Experiment tracking. Feature engineering. Deployment. Monitoring. That’s why this Python AI ecosystem map matters. It shows the tools that quietly run every serious AI system. Not just training models - but building systems that actually work. If you’re a developer trying to move from notebooks to real AI projects, this is the difference. Most tutorials teach models. Few teach the ecosystem. Save this - it’s a useful reference. Which of these tools have you actually used? [ python ai tools mlops python machine learning libraries python data science stack ai development tools python ml ecosystem ai engineering tools ml pipelines python ai frameworks ] #machinelearningtools #pythonforai #aiengineering #pythonlibraries #learnai
#Pytorch Reel by @michaellin250 - pure math majors often have deep theoretical knowledge, but that doesn't automatically translate into applied skills like machine learning. They might
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@michaellin250
pure math majors often have deep theoretical knowledge, but that doesn’t automatically translate into applied skills like machine learning. They might understand probability, linear algebra, or optimization in the abstract, but ML requires: 1. **Programming skills** – Python, TensorFlow, PyTorch, data pipelines. 2. **Data intuition** – cleaning, handling missing values, feature engineering. 3. **Applied statistics** – distributions, hypothesis testing, overfitting/underfitting. 4. **ML-specific algorithms** – decision trees, neural networks, clustering, reinforcement learning. 5. **Experimentation & deployment** – training models, tuning hyperparameters, and putting models into production. A pure math PhD can be brilliant at proofs and theory, but without hands-on ML practice, they often struggle to apply those concepts to real-world datasets. #coding #programming #development
#Pytorch Reel by @oreshihon.atsushi.iida - [New Feature] Real-time Body Load Analysis for Climbing using AI & Physics Engine 🧗‍♂️ Combined pose estimation AI with a biomechanics model to vi
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@oreshihon.atsushi.iida
[New Feature] Real-time Body Load Analysis for Climbing using AI & Physics Engine 🧗‍♂️ Combined pose estimation AI with a biomechanics model to visualize physical load on the body during climbing. — 🤖 Tech Stack Pose Estimation RTMPose (133-point keypoint detection)High-precision tracking down to fingertips and toes Contact Detection Velocity-based contact detection algorithmOnly stationary limbs are recognized as support pointsAirborne limbs are automatically excluded — ⚙️ Physics Calculations Support Force Distribution Inverse Distance Weighting (IDW) Body weight distributed based on distance from center of gravity to each support point Joint Torque τ = r × F × sin(θ) τ: Torque (Nm) r: Lever arm length F: Applied force θ: Joint flexion angle → Straight joint = zero load, more bend = higher load Body Segment Mass Ratios (Biomechanics Literature) Head: 8%Torso: 50%Upper arm: 2.7% × 2Forearm + hand: 2.3% × 2Thigh: 10% × 2Lower leg + foot: 5% × 2 — 📊 Output | Metric | Unit | Display Location | |———————|——|——————| | Hand support force | kg | Near wrist | | Foot support force | kg | Near ankle | | Shoulder torque | Nm | Shoulder joint | | Elbow torque | Nm | Elbow joint | | Hip torque | Nm | Hip joint | | Knee torque | Nm | Knee joint | Color Map: Green (low load) → Yellow (medium load) → Red (high load) — 💡 Applications Identify high-injury-risk movesLearn efficient weight transferMonitor training load Future plans include integration with musculoskeletal models like OpenSim for muscle force estimation. — #bouldering #climbing #rockclimbing #AI #MachineLearning #DeepLearning #PoseEstimation #RTMPose #MMPose #OpenMMLab #PyTorch #ComputerVision #Biomechanics #Physics #InverseKinematics #JointTorque #MotionCapture #MotionAnalysis #SportsScience #SportsTech #OpenCV #Python #CUDA #EdgeAI #RealTimeProcessing #KeypointDetection #HumanPoseEstimation #3DPose #DepthEstimation #IndieGameDev
#Pytorch Reel by @irieti - Deep Q-Network with PyTorch for ad creative selection optimization
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@irieti
Deep Q-Network with PyTorch for ad creative selection optimization
#Pytorch Reel by @oreshihon.atsushi.iida - 強傾斜だと腰と壁の距離が空きがちになって、次のホールドに移動する時に長い距離を移動できない場合が多発してる。 下半身の筋肉を強化する必要がある。 グラフで原因が分かりやすくなって良かった！ #bouldering #boulder #climb #climbing #climber #train
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@oreshihon.atsushi.iida
強傾斜だと腰と壁の距離が空きがちになって、次のホールドに移動する時に長い距離を移動できない場合が多発してる。 下半身の筋肉を強化する必要がある。 グラフで原因が分かりやすくなって良かった！ #bouldering #boulder #climb #climbing #climber #training #trainingday #sport #climbingtraining #climbingcoach #rocklands #computervision #sportstech #sportsscience #python #AI #poseestimation #mmpose #pytorch #opencv #rtmw3d #3dpose #groundingdino #zeroshot #radarchart #performanceanalysis
#Pytorch Reel by @pythonlogicreels - 🚀 TOP PYTHON MODULES YOU MUST KNOW IN 2026 🐍🔥 If you're learning Python or leveling up your coding game, these powerful modules can change everyth
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@pythonlogicreels
🚀 TOP PYTHON MODULES YOU MUST KNOW IN 2026 🐍🔥 If you're learning Python or leveling up your coding game, these powerful modules can change everything 💻⚡ 📊 Data Analysis & Visualization • Pandas • NumPy • Matplotlib • Seaborn • SciPy 🤖 Machine Learning & AI • Scikit-learn • TensorFlow • Keras • PyTorch • XGBoost 🌐 Web Development • Django • Flask • FastAPI • Requests • BeautifulSoup 🗄️ Database Access • SQLAlchemy • Psycopg2 • PyMySQL • SQLite3 • MongoEngine 🌐 Networking & Communication • Socket • Paramiko • Twisted • Flask-SocketIO • paho-mqtt ⚙️ System Administration & Utilities • OS • Subprocess • Pathlib • Argparse • shutil 💡 Whether you're into data science, AI, web development, or backend engineering, mastering these Python libraries will make you unstoppable 🚀 👉 Save this reel for later 👉 Share with your coding friends 👉 Follow for more Python & tech content . . . . . #pythonprogramming #codingquiz #pythonlogicreels #learnpython #codingchallenge
#Pytorch Reel by @sujar.tech (verified account) - comment "ML" for a lot of Machine learning resources that will help you while learning These are some really awesome machine learning projects that y
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@sujar.tech
comment “ML” for a lot of Machine learning resources that will help you while learning These are some really awesome machine learning projects that you can build to stand out, and you will benefit greatly when completing them It gives you a good overview of Neural Networks, PyTorch,Python, SpaCy(NLP),Preprocessing,Convolutional Neural Networks,Classifiers, Website Building(if you do the complex routes),Datasets,Training and Testing, and many more topics… #coding #computerscience #cs #machinelearning
#Pytorch Reel by @awsdevelopers (verified account) - 🔍 Curious about PyTorch? Let's break down this powerful machine learning framework in 64 seconds! PyTorch helps developers: 🚀 Build and train neur
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@awsdevelopers
🔍 Curious about PyTorch? Let's break down this powerful machine learning framework in 64 seconds! PyTorch helps developers: 🚀 Build and train neural networks faster 🔄 Create dynamic computational graphs 🎯 Deploy models with production-level performance ⚡️ Accelerate development with pre-trained models Perfect for: 📊 Data scientists exploring deep learning 💻 Developers building AI applications 🎓 Students learning machine learning Ready to supercharge your ML projects? Try the Amazon Graviton optimized binaries for PyTorch! Get started via link in bio 🔗 Follow @awsdevelopers for more cloud content ————————— 🏷 #AWS #MachineLearning #PyTorch #ArtificialIntelligence #CloudComputing
#Pytorch Reel by @dailymathvisuals - ReLU - the activation that revolutionized deep learning 🚀 f(x) = max(0, x) That's the whole formula. Beautifully simple. Why it works: 📐 Zero
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@dailymathvisuals
ReLU — the activation that revolutionized deep learning 🚀 f(x) = max(0, x) That's the whole formula. Beautifully simple. Why it works: 📐 Zero for negative inputs, linear for positive ⚡ Gradient = 1 (no sigmoid-style saturation) 🧮 No exponentials — blazing fast 📊 Up to 4× stronger gradient than sigmoid The catch? ⚠️ Dying ReLU — if a neuron goes negative, it stops learning forever. Fun fact: The derivative is undefined exactly at zero — but we handle it in practice! This simple "ramp" function made deep networks practical. Save this for later! 🔖 — Follow @dailymathvisuals for more math visuals ✨ #relu #activationfunction #neuralnetworks #machinelearning #deeplearning #ai #mathvisualized #datascience #pytorch #tensorflow #coding #programming #mathreels #learnwithreels #stem

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