README: Two Distinct Software Development Projects

This README provides an overview of two exciting software development projects. The first project is a Haskell-based web scraping library, and the second is a machine learning model for MNIST digit recognition using Keras and TensorFlow.

Table of Contents

1. Haskell Web Scraping Library
2. MNIST Digit Recognition with Keras

---

Haskell Web Scraping Library

Project Overview: This project aims to develop a Haskell library for web scraping. The library facilitates making requests to URLs of the user's choice and parsing strings with utility parser functionalities.

Key Features:

• URL Request Handling: Allows users to send requests to specified URLs and retrieve the data.
• String Parsing Utilities: The library includes functions to parse strings, such as splitting a string from one character to another, or based on a specified substring.

Use Cases:

• Extracting data from web pages for data analysis.
• Automating the collection of information from various websites.

Programming Language: Haskell

---

MNIST Digit Recognition with Keras

Project Overview: This project involves creating a machine learning model to recognize handwritten digits from the MNIST dataset using TensorFlow and Keras.

Project Lib Dependency:

python3 -m pip install tensorflow[and-cuda]
# Verify the installation:
python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"

Key Components:

• Data Preparation: Loading and preprocessing the MNIST Digit dataset.
• Model Architecture: Building a Sequential model with convolutional and dense layers.
• Training: Compiling and fitting the model with training data.
• Evaluation: Assessing model performance with test data.

Technologies Used:

• TensorFlow
• Keras

Use Cases:

• Digit recognition in images for automated data entry.
• Foundations for more complex image recognition tasks.

---