Ashhadul Islam

Ashhadul Islam

Harnessing AI to bridge visionary innovation with real-world impact.

Analyzing Satellite Images for Disasters

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In this post, I delve into the development of deep neural network models designed to analyze satellite images for disaster detection. This project was part of the AWS Disaster Response Hackathon, aiming to create a system capable of identifying and categorizing disaster-stricken regions based on satellite imagery.

The tutorial covers the process of pre-processing large image datasets, training deep neural networks for disaster detection, and improving the model’s efficiency by tailoring it to specific disaster types. Additionally, I demonstrate how to deploy the models using Streamlit, allowing for an interactive interface where users can analyze satellite images for signs of disaster damage.

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In the age of information, it’s easy to drown in the sea of notes we’ve meticulously collected over time. Imagine having a chat application that understands your notes and fetches the exact information you need, no matter how vast your repository is. Enter Falcon Mamba 7B and Retrieval-Augmented Generation (RAG) — a perfect blend of speed, precision, and language understanding that turns your note-taking chaos into order.

Balancing Regression Datasets with KNNOR-Reg Oversampling Technique

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Enhancing model performance in machine learning often begins with addressing the quality of your data. One of the most challenging issues is the imbalance in the target variable distribution, which can severely impact the accuracy of regression models.

MemGPT: Assimilating Information from Multiple PDFs

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In this post, I explore the capabilities of MemGPT in handling Retrieval-Augmented Generation (RAG) tasks, particularly focusing on its ability to assimilate information from multiple PDF files. While GPT-4 has its strengths, it often struggles with tasks requiring the consolidation of information from several documents. MemGPT, on the other hand, excels in this area, as demonstrated through a series of comparisons.

Advanced Retrieval with LlamaPacks: Elevating RAG in Fewer Lines of Code!

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In this post, I explore five Retrieval-Augmented Generation (RAG) methods open-sourced by LlamaIndex, which claim to simplify the RAG process to just about one line of code. Through a series of experiments, I test these methods and compare their effectiveness in terms of accuracy and efficiency.