Convolution in CNNs Explained | PyTorch Implementation + Animations (Full Tutorial)
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00:00 – Introduction to Convolution
06:12 – Convolution Animations
16:30 – Convolution using Pytorch
In this video, we dive deep into the core building block of Convolutional Neural Networks: We begin by understanding how an image is represented as a numeric array and how a 3×3 kernel (filter) slides over the input image to generate a feature map. Using clear examples, dot-product demonstrations, and step-by-step window shifting, you will learn how the output size is calculated using the formula (input size − kernel size + 1) and how padding can preserve dimensions. After visual understanding, we implement the convolution operation manually in NumPy using nested loops, patches, and dot products. Then we move toward real-world visuals with animations showing kernel projection, stride effects, and interactive filter demos. Finally, we rewrite the entire convolution process using PyTorch’s nn.Conv2d, explore image pre-processing, tensor dimensions, custom kernels, and view side-by-side original vs convolved images. This complete workflow takes you from concept → NumPy implementation → PyTorch implementation, preparing you for deeper CNN concepts ahead.
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