CNN Explained Visually: Padding, Stride, Pooling, Receptive Fields, Dilation & Layer Architecture
About this lesson
In this video, we understood the core building blocks of a convolutional neural network. We started with convolution and saw how filters extract features like edges, textures, and shapes from images. Then we looked at important concepts like padding and stride, and how they affect the output dimensions and the flow of information through the network. We also discussed receptive fields and saw how they grow across layers, allowing CNNs to move from detecting simple patterns in early layers to complex objects in deeper layers. Finally, we talked about dilated convolutions and how they help increase the receptive field without increasing the number of parameters. By the end of this video, you should have a clear intuition of how CNNs process images and why they work so well for visual tasks. Link for the animation codes:- https://github.com/ByteQuest0/Animation_codes/tree/main/2025/CNN Links for Important videos ✅ :- Neural Networks:- https://youtu.be/sE6OaMndGZg Gradient descent :- https://youtu.be/2xdUsy3oq-4 BackPropagation:- https://youtu.be/nAMkcgxKwfA Momemtum Gradient descent:- https://youtu.be/Q_sHSpRBbtw Data Normalization:- https://youtu.be/W2vqsTg-rDU 📚 Welcome to the Channel! If you're passionate about learning complex concepts in the simplest way possible, you're in the right place. I create visual explanations using animations to make topics more intuitive and engaging—especially in Algorithms, AI, machine learning, and beyond. 🎥 Animations created using Manim: Manim is an open-source Python library for creating mathematical animations. Learn more or try it yourself: 🔗 https://www.manim.community Let's Connect:- GitHub:- https://github.com/ByteQuest0 Reddit:- https://www.reddit.com/r/ByteQuest/
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