From 2fda722e3dcff8acfa8dfa9ce9544e1dd65eaa34 Mon Sep 17 00:00:00 2001 From: "Visual Computing (VICO) Group" Date: Mon, 14 Oct 2024 10:03:02 +0100 Subject: [PATCH] Minor update --- .gitignore | 2 ++ notebooks/Coursework_1.ipynb | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index ee3912c..97d5dbd 100644 --- a/.gitignore +++ b/.gitignore @@ -26,6 +26,7 @@ var/ *.egg-info/ .installed.cfg *.egg +etc/ # PyInstaller # Usually these files are written by a python script from a template @@ -68,6 +69,7 @@ notebooks/.ipynb_checkpoints/ # Data folder data/ +solutions/ # Latex stuff report/mlp-cw1-template.aux diff --git a/notebooks/Coursework_1.ipynb b/notebooks/Coursework_1.ipynb index e2d719c..3a9693d 100644 --- a/notebooks/Coursework_1.ipynb +++ b/notebooks/Coursework_1.ipynb @@ -6,7 +6,7 @@ "source": [ "# Coursework 1\n", "\n", - "This notebook is intended to be used as a starting point for your experiments. The instructions can be found in the MLP2023_24_CW1_Spec.pdf (see Learn, Assignment Submission, Coursework 1). The methods provided here are just helper functions. If you want more complex graphs such as side by side comparisons of different experiments you should learn more about matplotlib and implement them. Before each experiment remember to re-initialize neural network weights and reset the data providers so you get a properly initialized experiment. For each experiment try to keep most hyperparameters the same except the one under investigation so you can understand what the effects of each are." + "This notebook is intended to be used as a starting point for your experiments. The instructions can be found in the MLP2024_25_CW1_Spec.pdf (see Learn, Assignment Submission, Coursework 1). The methods provided here are just helper functions. If you want more complex graphs such as side by side comparisons of different experiments you should learn more about matplotlib and implement them. Before each experiment remember to re-initialize neural network weights and reset the data providers so you get a properly initialized experiment. For each experiment try to keep most hyperparameters the same except the one under investigation so you can understand what the effects of each are." ] }, {