A simple PyTorch implementation of various deep learning models.
Using Jupyter Notebook, you can easily understand the structure of the model and train it.
There are some differences from the official implementation:
- Due to resource limitations, the dataset used may be different.
- It may be slightly different from the training method shown in the paper.
| Model | Content | Dataset | Blog | Code |
|---|---|---|---|---|
| AlexNet (2012) [paper] | Alex Krizhevsky et al. "ImageNet Classification with Deep Convolutional Neural Networks" | CIFAR-10 | Blog | Code |
| VGGNet (2014) [paper] | Karen Simonyan et al. "VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION" | CIFAR-10 | Blog | Code |
| GoogLeNet (2014) [paper] | Christian Szegedy et al. "Going deeper with convolutions" | CIFAR-10 | Blog | Code |
| ResNet (2016) [paper] | Kaiming He et al. "Deep Residual Learning for Image Recognition" | CIFAR-10 | Code |