From f3f72bce44c4a78592d40e506ec367ba841db85a Mon Sep 17 00:00:00 2001
From: asytuyf <75701022+asytuyf@users.noreply.github.com>
Date: Sun, 2 Jul 2023 20:45:03 +0200
Subject: [PATCH] Minor adjustments to the documentation

1 - Clearer description of the problem being solved
2 - how to install tqdm as it is not a native library in python
---
 ML/Pytorch/Basics/pytorch_simple_fullynet.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/ML/Pytorch/Basics/pytorch_simple_fullynet.py b/ML/Pytorch/Basics/pytorch_simple_fullynet.py
index 36a399d8..4a34cb75 100644
--- a/ML/Pytorch/Basics/pytorch_simple_fullynet.py
+++ b/ML/Pytorch/Basics/pytorch_simple_fullynet.py
@@ -1,8 +1,9 @@
 """
 A simple walkthrough of how to code a fully connected neural network
-using the PyTorch library. For demonstration we train it on the very
-common MNIST dataset of handwritten digits. In this code we go through
-how to create the network as well as initialize a loss function, optimizer,
+using the PyTorch library. This code is designed to solve a classic problem in machine learning: recognizing handwritten digits. 
+We use the MNIST dataset, which contains images of handwritten digits from 0 to 9. 
+The goal is to train a neural network to recognize these digits with high accuracy.
+In this code we go through how to create the network as well as initialize a loss function, optimizer,
 check accuracy and more.
 
 Programmed by Aladdin Persson
@@ -22,7 +23,7 @@
 from torch.utils.data import (
     DataLoader,
 )  # Gives easier dataset managment by creating mini batches etc.
-from tqdm import tqdm  # For nice progress bar!
+from tqdm import tqdm  # For nice progress bar! (Type "pip install tqdm" in your Terminal if u don't have the module to install it)
 
 # Here we create our simple neural network. For more details here we are subclassing and
 # inheriting from nn.Module, this is the most general way to create your networks and