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⌚Time Stamps⌚
[00:00] - Recap
[03:50] - Plan of Action
[05:46] - Prerequisite
[07:10] - What is CNN
[13:36] - CNN Architecture & Code Demo
[16:17] - Modifying existing code for CNN architecture in PyTorch.
[17:17] - Converting 1D image data into 2D for CNN processing.
[19:03] - Building a CNN architecture divided into feature extraction and classification.
[20:07] - Adding layers for feature extraction in CNN architecture.
[21:59] - Constructing the first convolutional layer with filters and activation functions.
[23:08] - Duplicate the first layer to create a second convolutional layer.
[24:56] - Building the classification stage with hidden and flattened layers in CNN.
[25:48] - Understanding tensor dimensions for CNN input processing.
[28:01] - Creating a CNN architecture with hidden layers and activation functions.
[29:00] - Implementing dropout layers to prevent overfitting in CNN architecture.
[30:56] - CNN optimizes image data performance, achieving 99.9% accuracy
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