📃: Convolutional Neural Network

[amazingak1]

🔴 AIM :
To implement and provide comprehensive documentation for Convolutional Neural Networks (CNNs), covering their theoretical foundation, practical implementation, and use cases.

🔴 Brief Explanation :
This document involves creating detailed markdown files for CNNs. The documentation should include:

• An introduction to CNNs:
  • Definition of CNNs and their role in deep learning.
  • Overview of their architecture and working principles.
• Mathematical and conceptual background:
  • Explanation of convolution, kernels, strides, and padding.
  • Role of pooling layers (e.g., max pooling, average pooling).
  • Fully connected layers and activation functions.
• Use cases and advantages/disadvantages:
  • Common applications (e.g., image classification, object detection, etc.).
  • Benefits of feature extraction and reduced parameter count.
  • Limitations such as computational cost.
• Step-by-step implementation in Python:
  • Implementation using libraries like TensorFlow/Keras or PyTorch.
  • Explanation of the code with comments.
• Visualizations for better understanding:
  • Feature maps, filters, and activation visualizations.
  • Training/validation loss and accuracy graphs.

Screenshots 📷

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✅ To be Mentioned while taking the issue :

• Full name : Arpit Kumar
• What is your participant role? SWOC - Social Winter Of Code Season 5

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Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

[11PRIMUS]

@amazingak1 go for it