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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.
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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.
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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.
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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.
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In this post, I delve into the capabilities of Ollama, a powerful infrastructure that simplifies local execution of open-source models and interactions with PDFs. Ollama acts like a Docker system for Large Language Models (LLMs), allowing easy setup of local LLM servers, fine-tuning, and more.
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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.
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In this article, I explore the application of deep learning techniques to detect Monkeypox from images, a timely topic given the World Health Organization’s declaration of Monkeypox as a global health emergency. The project involves collecting and processing image data of various types of pox, including Chickenpox, Measles, and Monkeypox, to train models capable of distinguishing between these conditions.
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In this post, I take you through the process of building an object detection model using YOLOv5, specifically for detecting road damages from images and videos. This project demonstrates how to leverage transfer learning by training a deep neural network on your own dataset, allowing the model to detect specific objects of interest, such as abnormalities in X-Ray images or road damages in surveillance footage.
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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.
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In this article, I discuss the critical issue of imbalanced data in machine learning and introduce a novel augmentation technique designed to address this challenge. Even the most sophisticated models can fail if the underlying data is imbalanced, leading to misleadingly high accuracy rates while misclassifying minority cases.
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In this post, I tackle the common challenges faced when scraping websites, particularly the frustration of being blocked after making too many consecutive requests. I explore how to use Python, Selenium, and Tor to bypass these limitations, enabling you to continue scraping without interruptions.