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We've added 15 new Computer Vision problems. revise your concepts by coding them out: > max pooling > image rotation > gaussian blur kernel > sobel edge detection > image histogram > 2D convolution > IoU > Non-maximum supression etc

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TensorTonic

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35,683 просмотров • 6 месяцев назад •via X (Twitter)

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Most awaited Optimization techniques in Machine Learning module is out! We've covered the fundamentals of - > SGD variants > Convex vs Non-Convex > Momentum & Nesterov acc. > Adam, RMSprop > Loss landscapes > Regularization > Batch Norm > Newton's method & lagrange
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Most awaited Optimization techniques in Machine Learning module is out! We've covered the fundamentals of - > SGD variants > Convex vs Non-Convex > Momentum & Nesterov acc. > Adam, RMSprop > Loss landscapes > Regularization > Batch Norm > Newton's method & lagrange

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Our final module is out! We've covered the fundamentals of Information theory. topics include - > shannon entropy > KL divergence > information gain (decision trees) > mutual information > cross entropy loss > GANs training > Jensen-Shannon Divergence > Perplexity

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New open source image model called Mage-Flow by Microsoft. > Supports image generation and editing. > Lightweight 4B model with 4-step turbo variants. > Free and open-source, MIT license.
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Don't miss out 🎤 YouTube star Max Fosh brings new show Loophole on tour. Grab your tickets 🎟️ >> Max Fosh
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Galeria 1 is now available – an image viewer for React (+ Native) → New Architecture Support → Stable web support → Bring-your-own-image component Automatic support for: solito/image, next/image, expo-image, react-native-fast-image, web <img />
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Galeria 1 is now available – an image viewer for React (+ Native) → New Architecture Support → Stable web support → Bring-your-own-image component Automatic support for: solito/image, next/image, expo-image, react-native-fast-image, web <img />

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CAT3D + time => CAT4D! 🐈 Check out our latest work on turning text/image(s)/video into dynamic 3D models that one can explore in real time, led by brilliant Rundi Wu!

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I created a web app for teaching computer vision basics, allowing users to upload an image or video stream and instantly view it as a numerical pixel matrix, with options to display values in RGB, Hex, or Grayscale formats. It features predefined convolution kernels applied in real-time: - Sobel operator for edge detection (computes gradients to highlight boundaries) - Gaussian Blur: averages neighboring pixels with weights to reduce noise - Sharpen: boosts local contrast to enhance details This setup clearly visualizes how images are represented as matrices or tensors, how convolution slides kernels across pixels, and how operations like gradient computation, blurring, or sharpening modify the data step-by-step. I plan to use demos like this in my upcoming computer vision course to build intuition for fundamental image processing concepts, OpenCV filters, and CNN building blocks (e.g., why kernels extract features).
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I created a web app for teaching computer vision basics, allowing users to upload an image or video stream and instantly view it as a numerical pixel matrix, with options to display values in RGB, Hex, or Grayscale formats. It features predefined convolution kernels applied in real-time: - Sobel operator for edge detection (computes gradients to highlight boundaries) - Gaussian Blur: averages neighboring pixels with weights to reduce noise - Sharpen: boosts local contrast to enhance details This setup clearly visualizes how images are represented as matrices or tensors, how convolution slides kernels across pixels, and how operations like gradient computation, blurring, or sharpening modify the data step-by-step. I plan to use demos like this in my upcoming computer vision course to build intuition for fundamental image processing concepts, OpenCV filters, and CNN building blocks (e.g., why kernels extract features).

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If you've made >100 images with Midjourney you can now help us alpha-test image creation on our website. We've also added a 'rooms' feature where you can create in real-time with people around the world. Give it a shot and let us know what you think!

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Introducing Diagnose and Debug. Give your agent first-party backend context: health metrics, logs, errors, database checks, and more. Your coding agent catches backend issues now, then fixes them by working with your dedicated InsForge debugging agent. Agent <> Agent.

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My brother and I just launched an app! Nutrify, a Pokédex for food 📸 -> 🍍 Nutrify uses computer vision to identify different foods and display information about them. Try it on the iOS App Store for free today! Check out the launch video too! (more links below)

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Introducing SWE-grep and SWE-grep-mini: Cognition’s model family for fast agentic search at >2,800 TPS. Surface the right files to your coding agent 20x faster. Now rolling out gradually to Windsurf users via the Fast Context subagent – or try it in our new playground!

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