GoogLeNet Deep Neural Network Explained - Inception V1 Architecture and Implementation

Explore the intricacies of GoogLeNet, also known as Inception V1, in this comprehensive 23-minute tutorial. Dive into the architecture of this 22-layer deep neural network, which revolutionized deep learning in 2014 with its innovative use of 1x1 convolutions. Learn how to implement GoogLeNet using PyTorch and apply it to the CIFAR-10 dataset. Follow a detailed breakdown of the "Going Deeper with Convolutions" paper, understanding the Inception module, architecture details, training process, and results. Gain hands-on experience with PyTorch implementation, covering Inception modules, BasicConv2D, InceptionAux, and the complete GoogLeNet structure. Perfect for machine learning enthusiasts looking to deepen their understanding of influential neural network architectures.

- Introduction:
- GoogLeNet with Pytorch on CIFAR-10:
- Background:
- Architecture Overview:
- Inception Module:
- Architecture Details:
- GoogLeNet Training:
- GoogLeNet Result:
- GoogLeNet Pytorch Overview:
- GoogLeNet Pytorch - Inception:
- GoogLeNet Pytorch - BasicConv2D:
- GoogLeNet Pytorch - InceptionAux:
- GooGleNet Pytorch - GoogLeNet:
- Conclusion: