Vivek Menon M
vivekmenonm
A tech enthusiast exploring the frontiers of AI, Data Science, and Data Analytics.
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52
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11
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Python
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Top Repositories
A Streamlit-based app that lets users upload and chat with documents (PDF, DOCX, CSV, Excel) using LangChain, OpenAI, and FAISS. Includes user authentication, chat history, and PostgreSQL integration.
resume classifier that analyzes input resumes and provides a similarity score based on a given job description (JD). This classifier will be designed to determine how closely a resume matches the requirements and qualifications specified in the JD.
The Disease Classifier System is a sophisticated and reliable disease classification application designed for accurate disease prediction based on input symptoms. Utilizing state-of-the-art machine learning algorithms and medical expertise, this system analyzes symptom patterns to provide precise and reliable disease classification.
A threat detector classifier is a machine learning model designed to identify and classify potential threats within a given dataset. It analyzes various features and patterns to determine the presence of malicious activity or potential risks.
This application uses deep learning techniques to accurately classify brain tumor images. It has been trained on a diverse dataset, enabling it to predict the presence and type of tumors with high accuracy.
Repositories
52No description provided.
No description provided.
No description provided.
A Streamlit-based app that lets users upload and chat with documents (PDF, DOCX, CSV, Excel) using LangChain, OpenAI, and FAISS. Includes user authentication, chat history, and PostgreSQL integration.
No description provided.
resume classifier that analyzes input resumes and provides a similarity score based on a given job description (JD). This classifier will be designed to determine how closely a resume matches the requirements and qualifications specified in the JD.
No description provided.
The Disease Classifier System is a sophisticated and reliable disease classification application designed for accurate disease prediction based on input symptoms. Utilizing state-of-the-art machine learning algorithms and medical expertise, this system analyzes symptom patterns to provide precise and reliable disease classification.
A threat detector classifier is a machine learning model designed to identify and classify potential threats within a given dataset. It analyzes various features and patterns to determine the presence of malicious activity or potential risks.
No description provided.
No description provided.
This application uses deep learning techniques to accurately classify brain tumor images. It has been trained on a diverse dataset, enabling it to predict the presence and type of tumors with high accuracy.
A multilingual semantic search engine utilizing sentence similarity with sentence transformers-based models.
A System for Predicting Risk Levels (Low, Mid, High) Based on Age, Systolic Blood Pressure, Diastolic Blood Pressure, Blood Sugar Level, Body Temperature, and Heart Rate.
No description provided.
Palm api running in local and examples
No description provided.
Modular and ready to deploy code to detect and track videos using YOLO-v7 and DeepSORT
Single image dehazing using the GMAN network and its implementation in Tensorflow(version 2+).
Implement Image Dehazing Using Residual-Based Deep CNN paper with added refinements from Dehazenet
KandinskyVideo — multilingual end-to-end text2video latent diffusion model
A web interface for real-time yolo inference using streamlit. It supports CPU and GPU inference, supports both images and videos and uploading your own custom models.
Zephyr 7B beta RAG Demo inside a Gradio app powered by BGE Embeddings, ChromaDB, and Zephyr 7B Beta LLM.
No description provided.
Animal Detection using YOLOv5
This project is dedicated to information retrieval from self-owned PDFs, encompassing valuable data relevant to business, policy, and other pertinent domains. Leveraging open-source packages and libraries, our approach ensures seamless execution of this objective within your local environment.
No description provided.
This is a medical bot built using Llama2 and Sentence Transformers. The bot is powered by Langchain and Chainlit. The bot runs on a decent CPU machine with a minimum of 16GB of RAM.
No description provided.
Detecting silent model failure. NannyML estimates performance with an algorithm called Confidence-based Performance estimation (CBPE), developed by core contributors. It is the only open-source algorithm capable of fully capturing the impact of data drift on performance.