CNN Fundamentals: Powering Modern Vision Tasks
Oleg Tagobitsky Oleg Tagobitsky

CNN Fundamentals: Powering Modern Vision Tasks

Convolutional Neural Networks (CNNs) are the driving force behind many of the visual technologies we rely on every day — from unlocking our phones with facial recognition to enabling autonomous vehicles to understand their surroundings. But how do these networks actually work? In this beginner-friendly deep dive, we break down the core building blocks of CNNs, including convolutional layers, kernels, pooling, and activation functions. You'll learn how modern architectures like ResNet have overcome critical challenges to power advanced vision systems used in industries like retail, automotive, security and marketing. We’ll also explore real-world applications — such as OCR, background removal, logo detection and content moderation — and walk you through your options for training models, using pre-trained networks or deploying ready-to-use APIs. Whether you're just starting with deep learning or exploring how to bring AI vision into your product, this guide provides the clarity and insights you need to move forward with confidence.

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How Convolutional Neural Networks Transformed Industries Over the Past 10 Years
Oleg Tagobitsky Oleg Tagobitsky

How Convolutional Neural Networks Transformed Industries Over the Past 10 Years

Over the last decade, Convolutional Neural Networks (CNNs) have revolutionized industries by transforming how businesses process visual data. From automating product recognition in retail to enhancing medical diagnostics in healthcare, CNNs have become the driving force behind modern image processing solutions. This blog post explores key breakthroughs in CNN technology, real-world applications across sectors and emerging trends like real-time edge processing and multimodal AI systems. As industries continue to evolve, adopting AI-powered image processing strategies is becoming essential for businesses to stay competitive, streamline operations and unlock new opportunities for growth.

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