publish [GPU]: course_UvA-DL/02-activation-functions

Author: Azure Pipelines

.notebooks/course_UvA-DL/02-activation-functions.ipynb

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+oid sha256:e90d3ca314b85c23c45c86c6d64187530990a24ede67591f152042026ab68c28
+size 3237014

.notebooks/course_UvA-DL/02-activation-functions.jpg

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+title: 'Tutorial 2: Activation Functions'
+author: Phillip Lippe
+created: 2021-08-27
+updated: 2023-03-14
+license: CC BY-SA
+description: 'In this tutorial, we will take a closer look at (popular) activation
+  functions and investigate their effect on optimization properties in neural networks.
+
+  Activation functions are a crucial part of deep learning models as they add the
+  non-linearity to neural networks.
+
+  There is a great variety of activation functions in the literature, and some are
+  more beneficial than others.
+
+  The goal of this tutorial is to show the importance of choosing a good activation
+  function (and how to do so), and what problems might occur if we don''t.
+
+  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
+- lightning>=2.0.0
+accelerator:
+- CPU
+- GPU
+environment:
+- ipython==8.16.1
+- numpy==1.26.4
+- torchmetrics==1.2.1
+- torch==2.0.1
+- pytorch-lightning==2.0.9.post0
+- torchvision==0.15.2
+- lightning==2.3.3
+- seaborn==0.13.2
+- setuptools==69.0.3
+- matplotlib==3.8.4
+- urllib3==2.2.2
+published: '2024-07-19T19:22:02.189820'