1- Load MNSIT Dataset from sklearn
2- Preprocessing data using python libraries
3- Subset data to use only class 0 and class 1 for the next steps.
4- Standardize dataset.
5- Divide data into training and validation set.
6- Implement Logistic Regression with L1 Regularization.
7- calculate accuracy after applying L1 using different values for lampdas and learning rates.
8- Implement Logistic Regression with Mini Batch Optimizer.
9- calculate accuracy after applying Mini Batch using different values for lampdas and learning rates.
10- Implement Logistic Regression with RMS Prop Optimizer.
11- Calculate Accuracy after applying RMS.
12- Implement Logistic Regression with Adam Optimizer.
13- Calculate Accuracy after Applying Adam.
14- I wrote some conclusions for each case using Barplot.