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spark

spark-data-analysis-projects

A collection of data analysis projects done using PySpark via Jupyter notebooks.

spark-fast-tests

Apache Spark testing helpers (dependency free & works with Scalatest, uTest, and MUnit)

spark-in-local

Software and tools for setting up and operating a personal compute cluster, with focus on big data.

spark-standalone

spark-unzip

How to use zip and gzip files in Apache Spark

sparse-structured-attention

Sparse and structured neural attention mechanisms

spatial-vae

Source code for "Explicitly disentangling image content from translation and rotation with spatial-VAE" - NeurIPS 2019

spectral_inference_networks

Implementation of Spectral Inference Networks, ICLR 2019

sptag

A distributed approximate nearest neighborhood search (ANN) library which provides a high quality vector index build, search and distributed online serving toolkits for large scale vector search scenario.

spyder3_deeplearning

Spyder 3 : Customize for deeplearning

sqlalchemy

See the development link for contribution guidelines

sqlalchemy_aster

sqlalchemy_mf_aster

SQL Alchemy for Aster

srbench

A living benchmark framework for symbolic regression

stamp

Code for the KDD 2018 paper: STAMP: Short-Term Attention/Memory Priority Model for Session-based Recommendation

stan

STAN implementation according to paper 'Sequence and Time Aware Neighborhood for Session-based Recommendations'

starspace

Learning embeddings for classification, retrieval and ranking.

startupfiles

My IPython startup files.

statistical-learning

stepwisereg

Stepwise Regression-Python

stochastic

Generate realizations of stochastic processes in python.

stock-rnn

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.

streamlit

Streamlit — The fastest way to build custom ML tools

string_categorical_encoders

cardinality variables

stroke-controllable-fast-style-transfer

Code and data for paper: https://arxiv.org/abs/1802.07101

stylefusion

code/data for "Structuring Latent Spaces for Stylized Response Generation" (Gao et al., EMNLP'19)

stylegan2-pytorch

Simplest working implementation of Stylegan2 in Pytorch

sublime-howdoi-direct-paste

Search StackOverflow and paste code snippets without leaving Sublime Text