From d36336c264233fc3ea55a6c3bc8f5136cb961942 Mon Sep 17 00:00:00 2001
From: Danjou Pierre <pierre.danjou@etu.ec-lyon.fr>
Date: Mon, 11 Nov 2024 19:26:13 +0000
Subject: [PATCH] Update README.md

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 README.md | 22 +++++++---------------
 1 file changed, 7 insertions(+), 15 deletions(-)

diff --git a/README.md b/README.md
index 1ebe9c8..4bd266f 100644
--- a/README.md
+++ b/README.md
@@ -7,33 +7,25 @@ The objective of this tutorial is to write a complete image classification progr
 Two classification models will be successively developed and tested: k-nearest neighbors (KNN) and neural networks (NN).
 
 ## Prepare the CIFAR dataset
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 First of all, we had to prepare the CIFAR dataset. All the code can be found on the python file read_cifar.py
 
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 ## K-Nearest Neighbors (KNN)
 
 All the code can be found on the python file knn.py
 
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+Here is the graph of the accuracy of my knn code epending on the value of k for the Cifar dataset with a split factor of 0.9:
+``
 
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+## Artificial Neural Network
 
+### Maths 
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-## Artificial Neural Network
 
+​
 
+### Code
 
 
 
-- 
GitLab