#CNN

Articles about CNN — exploring patterns, best practices, and real-world implementations in production systems.

2 posts tagged with cnn. ← All posts

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Pratik Dhanave · ·6 min read

CNN Architectures — How Networks Got Deeper and Better

Once convolution proved it could crack vision, the race was on to build better CNNs — and the story of how they improved is a compact lesson in deep learning itself. From AlexNet to VGG to ResNet, each landmark architecture solved a specific problem that was blocking progress, and the solutions (depth, small filters, skip connections) became foundational ideas used far beyond vision. This post traces that evolution and the principles it taught.

From AlexNet (which launched deep learning) to VGG (small filters, deep) to ResNet (skip connections that made very deep networks trainable), each landmark solved a specific blocker. ResNet's residual connection generalized everywhere — including every transformer block — so the arc is a compact lesson in deep learning itself.

#Computer Vision#CNN#ResNet#Deep Learning#Machine Learning
Pratik Dhanave · ·6 min read

Convolutional Neural Networks — The Breakthrough That Cracked Vision

The convolutional neural network is the architecture that finally solved computer vision — and it did so with an idea as elegant as it is powerful: slide a small learnable filter across the image, everywhere, looking for patterns. CNNs automated the feature engineering that classical vision did by hand, learning a hierarchy from edges to objects directly from pixels. This post explains how convolution works and why it was exactly the right inductive bias for images.

The CNN solved vision by learning features instead of hand-designing them. Convolution slides a small learnable filter everywhere — local connectivity and parameter sharing give translation invariance for free — and stacking layers builds a hierarchy from edges to objects. The right inductive bias for images.

#Computer Vision#CNN#Deep Learning#Convolution#Machine Learning

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.