MACHINE LEARNING
This repository contains code and resources for building a Handwritten Digits Classifier using PyTorch. The goal of this project is to prototype a system for optical character recognition (OCR) on handwritten characters, specifically focusing on the MNIST database of handwritten digits.
Project Overview
As a machine learning engineer, I have been tasked with providing a proof of concept for the OCR system.
In this project, I preprocessed the MNIST dataset, built and trained a neural network using PyTorch, and fine-tuned the model for optimal performance.
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My GitHub contains some public and private repositories. Hope it helps!
Following up on my interests in Machine learning and Big Data, I explore various topics through research and practical applications. One such exploration is detailed in the paper titled:
Machine Learning Exploration of Bank Marketing Data with Apache Spark - IJRASET [PAPER ID: IJRASE8T61287]
Banks leverage advanced analytics provided by Apache Spark to enhance customer service and optimize marketing strategies. This paper integrates machine learning techniques to gain insights into consumer behavior through predictive modeling and efficient data processing. Key topics include customer segmentation, predictive modeling, and personalized marketing. PySpark's user-friendly interface and Spark's scalability support tactics related to growth, customer acquisition, and retention.
Keywords: Banks, Machine Learning, Predictive Modeling, Client Behavior, Marketing Strategies, Personalized Marketing, Data Processing, Scalability.
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