Popular GitHub Repositories for Collected Subdomains

A Burp Suite extension to add OpenAI (GPT) on Burp and help you with your Bug Bounty recon to discover endpoints, params, URLs, subdomains and more!

? awesome algorithm books I've collected 【不定期更新】 搜集整理的算法书籍（经典算法、ML/DL算法、面试算法、比赛算法等）

An eyeglass face dataset collected and cleaned for face recognition evaluation, CCBR 2018.

The repository has collected a batch of noteworthy MLSys bloggers (Algorithms/Systems)

Free subdomains for everyone.

Using the SWELL dataset from Kaggle, we've built 2 machine learning models to predict whether or not a person is under stress using Heart Rate Variability(HRV) which can be collected from modern wearables such as fitbit devices and apple watches.

Spatiotemporal datasets collected for network science, deep learning and general machine learning research.

This project walks you on how to create a twitter sentiment analysis model using python. Twitter sentiment analysis is performed to identify the sentiments of the people towards various topics. For this project, we will be analysing the sentiment of people towards Pfizer vaccines. We will be using the data available on Kaggle to create this machine learning model. The collected tweets from Twitter will be analysed using machine learning to identify the different sentiments present in the tweets. The different sentiments identified in this project include positive sentiment, negative sentiment and neutral sentiment. We will also be using different classifiers to see which classifier gives the best model accuracy.

Development of Deep Learning algorithms for Drivable Area Segmentation, Lane Segmentation, Traffic Sign Detection and Classification with data collected and labeled by Ford Otosan.

Code for processing and managing data for EEG-based emotion recognition of individuals with and without Autism. EEG and other clinical data were collected in StonyBrook Social Competence Treatment Lab, for data request evaluation please contact professor Matthew D. Lerner matthew.lerner@stonybrook.edu

Easy demo for finetuning a pre-trained Stable Diffusion XL with LoRA using the collected fashion dataset from scratch.

Development of Deep Learning algorithm for Drivable Area Segmentation with data collected and labeled by Ford Otosan.

Notebooks for detection and classification model training. Insect classification model. Python scripts for processing of data, collected with the Insect Detect DIY camera trap.

This project lets you create a chatbot of your own which would respond to text messages just like you would have. All you need to do is supply your WhatsApp chat backups (details in the README file) for training the model. No data is collected or shared with anybody. Note: This bot is not integrated with WhatsApp in any way and hence would not reply to your incoming texts automatically.

This project aims to perform sentiment analysis on tweets related to ChatGPT, a popular language model developed by OpenAI. The dataset used for training and testing consists of 219,293 tweets collected over a month. Each tweet is classified as positive ("good"), negative ("bad"), or ("neutral").

In this project, high dimensional noisy data collected from thousands of trucks during the course of 4 years was compressed using Artificial Neural Networks (ANN). This compressed meaningful information was used for performing predictive maintenance on turbochargers. Our novel deep learning ANN architecture, compressed vehicle data by over 87% while still improving fault forecasting prediction by 23% and even with extreme data size reduction of 99.7% we still see a significant performance improvement of 6.31%.

AWS Sample for extracting sensor data and detecting scenes from autonomous driving data collected in ROS bag files.

? this project aims to develop an app using an existing open-source LLM with data collected for domain-specific Jenkins knowledge that can be fine-tuned locally and set up with a proper UI for the user to interact with.

A thorough analysis of opensource Fitbit data is performed. The key findings are highlighted and discussed. The analysis provided herein is performed using 940 data points collected from 33 distinct users. Machine Learning Models are used to solve a regression problem using Multiple Linear Regression, Random Forest and Extreme Gradient Booster

Showcase how machine learning can help plant operator monitor equipment condition through correctly analyzing measurement data collected from many sensors.