[Original Paper - Very Deep Convolutional Networks for Large-Scale Image Recognition (2014)](https://arxiv.org/abs/1409.1556)  
[Related Video](https://www.youtube.com/watch?v=ACmuBbuXn20)

Some questions I had when I was reading the paper
- [What does 1x1 convolution mean in a neural network?](https://stats.stackexchange.com/questions/194142/what-does-1x1-convolution-mean-in-a-neural-network)
- [A guide to receptive field arithmetic for Convolutional Neural Networks](https://medium.com/mlreview/a-guide-to-receptive-field-arithmetic-for-convolutional-neural-networks-e0f514068807)

Some other useful links
- [VGGNet summary](https://medium.com/coinmonks/paper-review-of-vggnet-1st-runner-up-of-ilsvlc-2014-image-classification-d02355543a11)
- [VGGNet in Keras](https://towardsdatascience.com/step-by-step-vgg16-implementation-in-keras-for-beginners-a833c686ae6c)
- [VGGNet with Batch Normalization](https://gist.github.com/jjangsangy/38d644606130f05b806a4261c493a820)

This code is inspired by [VGGNet implement from scratch in PyTorch by aladdinpersson](https://github.com/aladdinpersson/Machine-Learning-Collection/blob/master/ML/Pytorch/CNN_architectures/pytorch_vgg_implementation.py).
