Popular GitHub Repositories for Historical Places

Using python and scikit-learn to make stock predictions

ARCHIVED: Contains historical course materials/Homework materials for the FREE MOOC course on "Creative Applications of Deep Learning w/ Tensorflow" #CADL

? Open Source App Framework to build streaming apps with real-time data - ? Build real-time data pipelines and make real-time data universally accessible - ? Join historical and real-time data in the stream to create smarter ML and AI applications. - ? Standardize complex data ingestion and stream data to apps with pre-built connectors

A list of AI Art courses, tools, libraries, people, and places.

Traffic prediction is the task of predicting future traffic measurements (e.g. volume, speed, etc.) in a road network (graph), using historical data (timeseries).

Keras code and weights files for the VGG16-places365 and VGG16-hybrid1365 CNNs for scene classification

Open Source REST API for named entity extraction, named entity linking, named entity disambiguation, recommendation & reconciliation of entities like persons, organizations and places for (semi)automatic semantic tagging & analysis of documents by linked data knowledge graph like SKOS thesaurus, RDF ontology, database(s) or list(s) of names

Stay ahead of AI trends with automated Reddit insights! ? This tool scans AI-related Reddit communities in both English and Chinese, using DeepSeek R1 to analyze posts and comments, summarize key discussions, and track historical trends. Daily rankings highlight the hottest topics, helping you catch emerging trends before they go mainstream.

Implementation of "GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings" in PyTorch

Keras implementation of "Image Inpainting via Generative Multi-column Convolutional Neural Networks" paper published at NIPS 2018

This is a project of build knowledge graph course. The project leverages historical stock price, and integrates social media listening from customers to predict market Trend On Dow Jones Industrial Average (DJIA).

Stock Market Trend Prediction using sentiment analysis Leveraging machine learning and sentiment analysis, we accurately forecast stock market trends. Our project combines advanced algorithms like BERT and Na?ve Bayes with sentiment analysis from Twitter and other sources. By analyzing sentiment and historical price data, we provide insights

Calculate technical indicators from historical stock data Create features and targets out of the historical stock data. Prepare features for linear models, xgboost models, and neural network models. Use linear models, decision trees, random forests, and neural networks to predict the future price of stocks in the US markets. Evaluate performance of the models in order to optimize them Get predictions with enough accuracy to make a stock trading strategy profitable.

In these notebooks the entire research and implementation process carried out for the construction of various machine learning models based on neural networks that are capable of predicting levels of solar radiation is captured given a set of historical data taken by meteorological stations.

This repo contains the code of a ai-trip-advisor, which generates trips with hotel recommendation, itineraries according to their budget and travel choice. Also i cannot add deployed link here because i used google maps places (NEW), photos, and geolocation api's due to which i have restriction of staying under expenditure of $200/monthly

Tools to train and explore diachronic word embeddings from Big Historical Data

Tutorial on NE processing for Digital Humanities - DH Utrech 2019

Stock Market Decision Support System using Deep Learning & Sentiment Analysis

An NER tool for ancient place names based on Pleiades and Spacy.

This project implements a time series multivariate analysis using RNN/LSTM for stock price predictions. A deep RNN model was created and trained on five years of historical Google stock price data to forecast the stock performance over a two-month period.