publish [GPU]: course_UvA-DL/04-inception-resnet-densenet

Author: Azure Pipelines

.notebooks/course_UvA-DL/04-inception-resnet-densenet.ipynb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f540213edde72f346da95fcdf3ff6dd6ea50457d83f7aef6218af166a66bb52a
+size 343823

.notebooks/course_UvA-DL/04-inception-resnet-densenet.jpg

@@ -0,0 +1,53 @@
+title: 'Tutorial 4: Inception, ResNet and DenseNet'
+author: Phillip Lippe
+created: 2021-08-27
+updated: 2023-03-14
+license: CC BY-SA
+tags:
+- Image
+description: '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.
+
+  '
+requirements:
+- torchvision
+- matplotlib
+- seaborn
+- tabulate
+- lightning>=2.0.0
+- tensorboard
+accelerator:
+- GPU
+environment:
+- setuptools==69.0.3
+- lightning==2.3.3
+- seaborn==0.13.2
+- urllib3==2.2.2
+- tensorboard==2.17.0
+- torchmetrics==1.2.1
+- numpy==1.26.4
+- ipython==8.16.1
+- torchvision==0.15.2
+- tabulate==0.9.0
+- pytorch-lightning==2.0.9.post0
+- matplotlib==3.8.4
+- torch==2.0.1
+published: '2024-07-19T19:35:09.316261'