BETA: Real-time inference from scalable machine learning in Python
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Updated
May 7, 2019 - Python
BETA: Real-time inference from scalable machine learning in Python
Code for "Training models when data doesn't fit in memory" post
A Parallel segmentation algorithm of a flowers dataset using Dask.
Word2vec for large corpus for Bangle
In this repo, I build a LogisticRegression prediction model with Dask and PySpark and initialize an AWS EMR cluster to run the entire pipeline.
Dask-parallelized project, contrasting GaussianNB and LightGBM models for EMNIST handwritten character classification.
Rapidsai_Machine_learnring_on_GPU
Saturn Cloud workshop on using LightGBM with Dask
Dask tutorial;Dask汉化教程
Sentiment analysis on hotel reviews, using MongoDB, applying Dask parallel programming, comparing Recurrent and Convolutional neural networks and visualizating with Dash.
The following project shows and compares machine learning between Pandas DataFrames and Dask Dataframes.
Build ColumnTransformers (Scikit or DaskML) for feature transformation by specifying configs.
Adds partial fit method to sklearn's forest estimators to allow incremental training without being limited to a linear model. Works with Dask-ml's Incremental.
Solution to kaggle competition OTTO – Multi-Objective Recommender System: https://www.kaggle.com/competitions/otto-recommender-system
Fraud detection ML pipeline and serving POC using Dask and hopeit.engine. Project created with nbdev: https://www.fast.ai/2019/12/02/nbdev/
Scaling ML models with Taipy and Dask
A Dask native implementation of 'Term Frequency Inverse Document Frequency' for dask-ml and scikit-learn
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