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mejihero

mejihero

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mejihero's repositories

Alink

Alink is the Machine Learning algorithm platform based on Flink, developed by the PAI team of Alibaba computing platform.

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anomaly-detection-resources

Anomaly detection related books, papers, videos, and toolboxes

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Bao_Catboost

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Bao_Demo_Test

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Bao_EOW

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Bao_Exam_ToyDataset

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Bao_MaskRCNN_Linux_car_damage_detector_1

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Bao_Retention_Demo

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Bao_Threshold

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Bao_Titanic_dataset

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Bao_Win_MaskRCNN_Demo

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Bao_Win_MaskRCNN_Demo_2

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Baoxian_OCR

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Bert-Multi-Label-Text-Classification

This repo contains a PyTorch implementation of a pretrained BERT model for multi-label text classification.

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cntlm-1

Cntlm is an NTLM / NTLM Session Response / NTLMv2 authenticating HTTP proxy intended to help you break free from the chains of Microsoft proprietary world. More info on http://cntlm.sourceforge.net/ website. THIS VERSION SUPPORTS SSPI, WHICH ALLOWS USERS WITH SMARTCARD AUTHENTICATION TO USE IT ON WINDOWS BOXES!

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Hands-On-Artificial-Intelligence-for-Banking

Hands-On Artificial Intelligence for Banking, published by Packt

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https-github.com-mejihero-learngit

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Industrial-Defect-Inspection-segmentation

Automatic Defect Inspection with End-to-End Deep Learning

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interpret

Fit interpretable machine learning models. Explain blackbox machine learning.

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learngit

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lit

The Language Interpretability Tool: Interactively analyze NLP models for model understanding in an extensible and framework agnostic interface.

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mlflow

Open source platform for the machine learning lifecycle

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News_Classification

News Classification

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notes

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object_detection_demo_segmentation_

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rightmove_webscraper.py

Python class to scrape data from rightmove.co.uk and return listings in a pandas DataFrame object

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Stocks

Programs for stock prediction and evaluation

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streamlit

Streamlit — The fastest way to build custom ML tools

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vaex

Out-of-Core DataFrames for Python, visualize and explore big tabular data at a billion rows per second.

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xgboost-virtual-data-science-seminar

XGBoost Insurance Pricing code comparing results with GLM and GAM for Actuarial Virtual Data Science Seminar

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