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springtricks

Sample Project for _40 Tips and Tricks for Spring in IntelliJ IDEA_

sq_signals

sql-more-queries-lab-nyc-career-ds-062518

sql_nosql

Issues to learn basic sql NoSql

sqlite3-dbf

Converter of XBase/FoxPro tables to SQLite

sqlixx

SQLite C++ driver

sqlmodel

SQL databases in Python, designed for simplicity, compatibility, and robustness.

sqrl

Minimal SQRL WIP

sr-gnn

[AAAI 2019] Source code and datasets for "Session-based Recommendation with Graph Neural Networks"

sshagentlib

.NET library creating ssh agent and client applications (works with both PuTTY/Pageant and OpenSSH)

sslcontext-kickstart

🔐 A lightweight high level library for configuring a http client or server based on SSLContext or other properties such as TrustManager, KeyManager or Trusted Certificates to communicate over SSL TLS for one way authentication or two way authentication provided by the SSLFactory. Support for Java, Scala and Kotlin based clients with examples. Available client examples are: Apache HttpClient, OkHttp, Spring RestTemplate, Spring WebFlux WebClient Jetty and Netty, the old and the new JDK HttpClient, the old and the new Jersey Client, Google HttpClient, Unirest, Retrofit, Feign, Methanol, Vertx, Scala client Finagle, Featherbed, Dispatch Reboot, AsyncHttpClient, Sttp, Akka, Requests Scala, Http4s Blaze, Kotlin client Fuel, http4k Kohttp and Ktor. Also gRPC, WebSocket and ElasticSearch examples are included

sstic-2016

Files for SSTIC 2016 challenge, in French

stackoverflow-pandas-canonicals

A directory listing of all my pandas canonicals on Stack Overflow to date.

stanford-tensorflow-tutorials

This repository contains code examples for the Stanford's course: TensorFlow for Deep Learning Research.

stat_rethinking_2022

Statistical Rethinking course winter 2022

state-of-the-art-result-for-machine-learning-problems

This repository provides state of the art (SoTA) results for all machine learning problems. We do our best to keep this repository up to date. If you do find a problem's SoTA result is out of date or missing, please raise this as an issue or submit Google form (with this information: research paper name, dataset, metric, source code and year). We will fix it immediately.

stateofthedotnetperformance

benchmarks and code samples for my State of the .NET Performance talk

stationarizer

Smart, automatic detection and stationarization of non-stationary time series data.

stats-maths-with-python

General statistics, mathematical programming, and numerical/scientific computing scripts and notebooks in Python

stein-variational-gradient-descent

code for the paper "Stein Variational Gradient Descent (SVGD): A General Purpose Bayesian Inference Algorithm"

stellarium

Stellarium is a free GPL software which renders realistic skies in real time with OpenGL. It is available for Linux/Unix, Windows and macOS. With Stellarium, you really see what you can see with your eyes, binoculars or a small telescope.

stitch

Python Remote Administration Tool (RAT)

stock

stock-management-system

It is a desktop application. Admin can entry category, company, stock in, stock out all information easily and view individual report.

stock-market-forecasting

Forecasting directional movements of stock prices for intraday trading using LSTM and random forest

stock-prediction-models

Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations

stockanalysis

Analysis of Individual Stock using pandas

stockmarketpredictionswithrnn

The goal of this project was to predict stock market prices using a recurrent neural network. This project was inspired by the Standford paper "Financial Market Time Series Prediction with Recurrent Neural Networks Armando Bernal, Sam Fok, Rohit Pidaparthi December 14, 2012" . Using the Standford paper as a baseline, I was able to improve and beat the accuracy of their model using hyper parameter tuning of my RNN network.

stockprediction

Data and code of my Medium story on stock prediction with TensorFlow

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.