This repository is a curated collection of Jupyter notebooks that serve as a comprehensive guide to understanding Convolutional Neural Networks (CNNs). Beginning with foundational concepts and parameters, the notebooks progressively dive into the intricacies of popular CNN architectures. Starting with LeNet-5 and advancing through AlexNet, Network in Network (NiN), and VGG, each notebook seamlessly blends theoretical insights with hands-on exercises. Whether you're a newcomer to CNNs or an enthusiast looking to deepen your knowledge, these notebooks provide an engaging learning experience that bridges theory and practice.
- LeNet-5
- AlexNet
- Network in Network (NiN)
- VGG
- Xception
- DenseNet
- SqueezeNet
- ResNext
- MobileNet
- ShuffleNet
- SeNet
- EfficientNet
This collection is continually expanding, with upcoming notebooks dedicated to further architectures, including Xception, DenseNet, SqueezeNet, ResNext, MobileNet, ShuffleNet, SeNet, and EfficientNet. Each notebook in this series is crafted to facilitate a practical understanding of the respective architecture, making it a valuable resource for anyone keen on mastering the realm of Convolutional Neural Networks.