Adaptive foreground-background segmentation using Gaussian Mixture Models (GMMs)
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Updated
Sep 6, 2018 - Jupyter Notebook
Adaptive foreground-background segmentation using Gaussian Mixture Models (GMMs)
在这个项目中,你将使用分类模型通过原料的不同组合预测所属的世界菜系。你将使用之前课程中所学习到的模型训练、测试技巧,并得到最终的分数,上传到 Kaggle 网站上。
在该项目中,你将使用强化学习算法,实现一个自动走迷宫机器人。
Personal capstone project for Udacity's Machine Learning Nanodegree (MLND).
Boston Housing Prices -- Udacity MLND
p0-Titanic - Udacity Machine Learning nanodegree (MLnd)
Implementing an image classifier with TensorFlow to recognize different species of flowers.
Udacity机器学习进阶,非监督学习,创建用户分类
Projects for Udacity Machine Learning Nanodegree.
Finding Charity Donors -- Udacity MLND
In this project I will employ several supervised algorithms to accurately model individuals' income using data collected from the 1994 U.S. Census.
Creating Customer Segments -- Udacity MLND
Udacity MLnd Boston Housing project
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