Reddit Data Pipeline (with airflow and AWS)

This project provides a comprehensive data pipeline solution to extract, transform, and load (ETL) Reddit data into a Redshift data warehouse. The pipeline leverages a combination of tools and services including Apache Airflow, Celery, PostgreSQL, Amazon S3, AWS Glue, Amazon Athena, and Redshift.

COVID-19 detection using Chest X-Ray Images

In our project, we employed transfer learning techniques to train convolutional neural network (CNN) models for the purpose of detecting COVID-19. We leveraged the power of pre-trained architectures, specifically VGG16, VGG19, and DenseNet121, to expedite the model training process and benefit from

LLM-Prompt-Engineering

This project explores the use of **prompt engineering** to improve dialogue summarization using large language models (LLMs). It evaluates the impact of different inference methods—zero-shot, one-shot, and few-shot—on the quality of model-generated summaries. By experimenting with various prompt str

Finetuning OpenAI GPT 3.5 Turbo

This project demonstrates fine-tuning OpenAI's GPT-3.5 Turbo model using a custom dataset in Google Colab, enabling the model to perform more specialized tasks based on specific requirements. The project covers dataset preparation, fine-tuning, and usage of the customized model for improved perform