Tutorial notebooks

Currently Playing: Tutorial 5: Inception, ResNet and DenseNet (Part 2)

In this tutorial, we will implement and discuss variants of modern CNN architectures. There have been many different architectures been proposed over the past few years. Some of the most impactful ones, and still relevant today, are the following: GoogleNet (https://arxiv.org/abs/1409.4842)/Inception architecture (winner of ILSVRC 2014), ResNet (https://arxiv.org/abs/1512.03385) (winner of ILSVRC 2015), and DenseNet (https://arxiv.org/abs/1608.06993) (best paper award CVPR 2017). All of them were state-of-the-art models when being proposed, and the core ideas of these networks are the foundations for most current state-of-the-art architectures. Thus, it is important to understand these architectures in detail and learn how to implement them. This notebook is part of a lecture series on Deep Learning at the University of Amsterdam. The full list of tutorials can be found at https://uvadlc-notebooks.rtfd.io. Link to the notebook: https://uvadlc-notebooks.readthedocs.io/en/latest/tutorial_notebooks/tutorial5/Inception_ResNet_DenseNet.html 00:00 Inception 03:31 GoogleNet 06:23 Tensorboard 09:07 ResNet blocks 13:50 ResNet model 15:50 SGD on ResNet 17:20 Training and TensorBoard