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Showing posts with the label convolution neural network

Unusual Traffic, Unexpected Chaos: The Truth Behind the Cloudflare Outage

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Cloudflare's Global Outage: Understanding the Root Cause Behind the September 18 Network Disruption On 18 September, the world witnessed a sudden and widespread slowdown of the internet. Websites and applications depending on Cloudflare ranging from social networks to API driven platforms began returning Internal Server Error messages. Millions of users assumed it was a cyberattack, a DDoS event, or a breach. However, the truth behind the outage was far more nuanced and rooted in Cloudflare’s internal architecture. This blog breaks down what Cloudflare does, why bot mitigation plays a critical role, how an unexpected configuration file led to an internal service crash, and why “unusual traffic” triggered a global ripple. What Cloudflare Really Does for the Internet Cloudflare is more than a simple CDN or firewall it is a massive reverse-proxy network that sits between users and websites. It accelerates content delivery, filters malicious traffic, provides DNS services, manage...
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Convolution Neural Network

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What is a Convolutional Neural Network (CNN)?      A Convolutional Neural Network (CNN) is a type of deep learning model specifically designed to process visual data like images and videos. It works by automatically learning patterns such as edges, textures, shapes, or objects from images without needing manual feature engineering. CNNs are inspired by the way the human visual cortex works and are widely used in computer vision tasks like image classification, object detection, face recognition, and more. CNNs reduce the complexity of image data using a structure of layers that include convolution layers, pooling layers, and fully connected layers. Each layer plays a unique role in extracting, simplifying, and interpreting the visual features. By stacking multiple such layers, CNNs can identify complex patterns and even recognize complete objects. One major advantage is that CNNs learn spatial hierarchies—starting from small patterns like edges to entire objects. CNNs ha...
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