Apache Spark - A unified analytics engine for large-scale data processing
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Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
Make Your Company Data Driven. Connect to any data source, easily visualize, dashboard and share your data.
The interactive graphing library for Python :sparkles:
List of Data Science Cheatsheets to rule the world
Suite of tools for deploying and training deep learning models using the JVM. Highlights include model import for keras, tensorflow, and onnx/pytorch, a modular and tiny c++ library for running math code and a java based math library on top of the core c++ library. Also includes samediff: a pytorch/tensorflow like library for running deep learn...
1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
Python SQL Parser and Transpiler
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
the portable Python dataframe library
macOS development environment setup: Easy-to-understand instructions with automated setup scripts for developer tools like Vim, Sublime Text, Bash, iTerm, Python data analysis, Spark, Hadoop MapReduce, AWS, Heroku, JavaScript web development, Android development, common data stores, and dev-based OS X defaults.
TensorFlowOnSpark brings TensorFlow programs to Apache Spark clusters.
A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).
MLeap: Deploy ML Pipelines to Production
Hopsworks - Data-Intensive AI platform with a Feature Store
PySpark + Scikit-learn = Sparkit-learn
ADAM is a genomics analysis platform with specialized file formats built using Apache Avro, Apache Spark, and Apache Parquet. Apache 2 licensed.
Pandas, Polars, Spark, and Snowpark DataFrame comparison for humans and more!
Open Lakehouse Compute Fabric: an open-source lakehouse platform with Microsoft Fabric Compute Extension.
A pure Python implementation of Apache Spark's RDD and DStream interfaces.
Formal specification and generation of verifiable binary parsers, message generators and protocol state machines
Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.
Helpers & syntactic sugar for PySpark.