rpatil524 Goto Github PK

sit

Systematic Investor Toolkit

smart-scheduling-links

Clinical Appointment Slot Discovery

solid

Solid - Re-decentralizing the web (project directory)

solid-nextcloud

A plugin to make Nextcloud compatible with Solid

solid-pg

Solid Playground - Solid Single-Page Playground Application

solid-slack

solid-vc

solidos

The operating system for Solid

soweego

Link Wikidata items to large catalogs

spacetimeformer

Multivariate Time Series Forecasting with efficient Transformers. Code for the paper "Long-Range Transformers for Dynamic Spatiotemporal Forecasting."

spacy

💫 Industrial-strength Natural Language Processing (NLP) with Python and Cython

spark

Apache Spark - A unified analytics engine for large-scale data processing

spark-bdp-stockticker-demo

A Python project that demonstrates Apache Spark on the Basho Data Platform

spark-nlp

State of the Art Natural Language Processing

spark-notebook

Interactive and Reactive Data Science using Scala and Spark.

spark-py-notebooks

Apache Spark & Python (pySpark) tutorials for Big Data Analysis and Machine Learning as IPython / Jupyter notebooks

spark-timeseries

A library for time series analysis on Apache Spark

spark-wiki-parser

A Spark based framework for parsing and extracting Media Wiki dumps (Wikipedia, Wiktionary, Wikidata)

sparql-visualizer

A typescript library to simplify visualization of SPARQL queries.

specification

Solid Technical Reports

spektral

Graph Neural Networks with Keras and Tensorflow.

stare

EMNLP 2020: Message Passing for Hyper-Relational Knowledge Graphs

stats

Scala Technical Analysis Tools

stellargraph

StellarGraph - Machine Learning on Graphs

stock-knowledge-graph

利用网络上公开的数据构建一个小型的证券知识图谱/知识库

stock-trend-predictor

Use Dynamic time warping algorithm and Hadoop MapReduce to predict the future stock trend

stock_surface

Machine Learning Algorithm To Predict Stock Direction

stockpredictionai

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

stockpredictionrnn

High Frequency Trading Price Prediction using LSTM Recursive Neural Networks

stocktrends

A python package to calculate trends in stocks, derivates(Futures & Options) using Renko, PnF, LineBreak etc