Repository Overview

This repo is for some projects on deep learning, machine learning, and programming exercises. They can be a bit messy because I didn't intend to publish them when I worked on them. So, please foregive me for that! In some files, I just wanted to squeez as much code in as possilbe so that I wouldn't need to go back and read a whole book again. Some files may appear too simple but they are not any more simple than the core idea is. Sometimes the core idea is all that there is; the rest is really commentary. Such is the case with some DL/ML models. So these codes can be very useful if you fall off track sometimes. I have organized them into the following categories:

Folder Structure

1. Data Visualization:

   • GRAPH.ipynb: A notebook for creating and analyzing graphs. It’s all about telling stories with data.
   • MatplotlibCourse.py: A script packed with examples to learn Matplotlib. It covers scatter plots, histograms, and how to make your plots look awesome.
   • Numpy6.py: A script that uses NumPy to do some math magic, like finding roots and extrema of a polynomial, and visualizing them.
   • Visualization_Primes.py: A cool way to see how prime numbers are distributed. It uses line plots and histograms to show the patterns. The numbers in First_1000_Prime_Numbers.txt are used here.
   • Visualization_Primes2.py: Another take on visualizing primes, but this time with a grid and custom colors. It’s pretty unique! The numbers in First_1000_Prime_Numbers.txt are used here too.
   • First_1000_Prime_Numbers.txt: A handy file with the first 1000 prime numbers. Great for math experiments or just for fun.

2. Deep Learning and Machine Learning:

   • FashionMNIST_Model.py: A fun project where we train a neural network to recognize clothing items from the Fashion MNIST dataset. It covers everything from data prep to training and testing the model.
   • Intel_Image_Classification.py: This script dives into image classification using the Intel Image Dataset. It uses a CNN to classify images and includes cool tricks like data augmentation. The dataset for this project can be found at Kaggle.
   • Machine Learning & Data Processing.ipynb: A mix of machine learning goodies! It has KNN, linear regression, logistic regression, and even regularized models like Lasso and Ridge. Plus, there’s some data visualization thrown in.
   • MachineLearning.py: A versatile script covering multiple machine learning algorithms, including KNN, linear regression, logistic regression, and regularized models like Lasso and Ridge. It also includes data visualization and evaluation metrics.
   • transfer_learning.ipynb: A notebook that shows how to use pre-trained models to solve new problems. Heads up: I didn’t make this one; I found it online.

3. Various Other Projects:

   • mergeSortDescending.py: A simple script that sorts a list in descending order using the merge sort algorithm. It’s all about divide and conquer!
   • OptimizedPython.py: A better version of the bubble sort algorithm. It’s faster because it stops early if the list is already sorted.
   • Pandas.py: A hands-on script for learning Pandas. It covers Series, DataFrames, and how to manipulate and visualize data.
   • printTwoElements.py: This one finds the repeating and missing numbers in a list. It has two approaches: a basic one and a smarter one using sorting and sums.

4. Documentation:

   • README.md: This file! It gives you a quick tour of what’s in the repo and what each file does.

Important Note

Please note that these files are from 2023, so they may not reflect the latest practices or updates.