Ashhadul Islam

Ashhadul Islam

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

Intel OneAPI — Detecting Weed in Crops

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In this post, I discuss my participation in the Intel® oneAPI Hackathon for Open Innovation, where the challenge was to detect weeds in cropland images using computer vision and deep learning techniques. The goal of this project was to develop a solution that allows for the targeted application of pesticides, minimizing environmental impact while maximizing crop yield.

Leveraging Intel’s oneAPI DevCloud, I demonstrate how to use YOLOv5 for weed detection and share insights into the overall development process. This solution aims to reduce the indiscriminate use of pesticides, which poses health risks, by enabling precise weed management.

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Chat with Your Obsidian Notes Using the Falcon Mamba 7B Model

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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.