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aust-hansen's Projects

machine-learning-learning-notes icon machine-learning-learning-notes

周志华《机器学习》又称西瓜书是一本较为全面的书籍,书中详细介绍了机器学习领域不同类型的算法(例如:监督学习、无监督学习、半监督学习、强化学习、集成降维、特征选择等),记录了本人在学习过程中的理解思路与扩展知识点,希望对新人阅读西瓜书有所帮助!

machinelearning-2 icon machinelearning-2

Machine Learning in Action学习笔记,一个文件夹代表一个算法,每个文件夹包含算法所需的数据集、源码和图片,图片放在pic文件夹中,数据集放在在Data文件夹内。书中的代码是python2的,有不少错误,这里代码是我用python3写的,且都能直接运行

machine_learning_and_application_v1 icon machine_learning_and_application_v1

本项目代码为雷明老师18年12月在清华出版社出版的《机器学习与应用》第一版中的配套代码,欢迎登录www.sigai.cn试听雷老师《机器学习》《深度学习》视频

magenta icon magenta

Magenta: Music and Art Generation with Machine Intelligence

mask_rcnn icon mask_rcnn

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

mav_tools_public icon mav_tools_public

General launch files, parameters and wiki entries on our systems and related issues

meka icon meka

Multi-label classifiers and evaluation procedures using the Weka machine learning framework.

ml-in-action-code-and-note icon ml-in-action-code-and-note

:chart_with_upwards_trend:Machine Learning code in Python3.x. Some notes about the practices please click here:(personal notes, for reference only)

ml-surveys icon ml-surveys

📋 Survey papers summarizing advances in machine learning.

ml-visuals icon ml-visuals

🎨 ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.

mlbook icon mlbook

Repository for the free online book Machine Learning from Scratch (link below!)

mlkp icon mlkp

CVPR18 Paper: Multi-scale Location-aware Kernel Representation for Object Detection

mmkd icon mmkd

This is the implementation for the ICME-2023 paper (Adaptive Multi-Teacher Knowledge Distillation with Meta-Learning).

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