CNN Output Size Formula Explained: Convolution, Stride, and Padding

How do you calculate the output size of a CNN layer? In Part 11 of my Machine Learning Exam Prep series, we cover Convolutional Neural Networks.

I explain how Filters (Kernels) work, the difference between Stride and Padding, and we use the "Golden Formula" to solve a dimension calculation problem by hand.

TIMESTAMPS:
0:00 - Intro: Why we need CNNs for Images
0:50 - What is a Filter (Kernel)?
1:32 - Stride vs. Padding Explained
2:25 - The Output Dimension Formula (Memorize This!)
3:13 - Hand-Solved Example: Calculating Output Height/Width
4:14 - Max Pooling Layers Explained (Downsampling)
5:05 - Summary: Conv vs Pool

THE FORMULA:
Output = [(Input - Filter + 2*Padding) / Stride] + 1

#DeepLearning #CNN #ConvolutionalNeuralNetworks #ComputerVision #MachineLearning #ExamPrep #DataScience

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