以MovieLens 的 ml-100k 为实验数据,基于 ItemCF 算法的推荐结果。
2022-01-21 09:15:25 19.64MB 算法
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以MovieLens 的 ml-100k 为实验数据,基于 ItemCF 算法作推荐,实现代码。
2022-01-21 09:15:25 15KB mapreduce hadoop
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MLKit示例 一组快速入门示例,这些示例演示了Android和iOS上的 API。 注意:由于此仓库的工作方式,我们不再直接接受拉取请求。 相反,我们将在内部对其进行修补,然后将其同步出去。
2022-01-18 15:31:08 63.75MB google translation barcode text-recognition
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两配对样本, 麦克尼马尔检验(McNemar test)等. 介绍可查看: https://blog.csdn.net/orDream/article/details/122540099
2022-01-17 19:15:01 1.34MB ML
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预测机票价格的Web应用程序
2022-01-15 11:45:41 1.64MB JupyterNotebook
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Vivado ML 2021 百度网盘链接.txt
2022-01-12 15:28:05 84B vivado_ML
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spring1718-Assignment3 --cs231n's the newest source code and learning source
2022-01-11 13:21:21 3.89MB cs231n ai ml prml
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机器学习PPT模板 [ML Visuals](https://docs.google.com/presentation/d/11mR1nkIR9fbHegFkcFq8z9oDQ5sjv8E3JJp1LfLGKuk/edit?usp=sharing) is a new collaborative effort to help the machine learning community in improving science communication by providing free professional, compelling and adequate visuals and figures. Currently, we have over 100 figures (all open community contributions). You are free to use the visuals in your machine learning presentations or blog posts. You don’t need to ask permission to use any of the visuals but it will be nice if you can provide credit to the designer/author (author information found in the slide notes). Check out the versions of the visuals below. This is a project made by the [dair.ai](https://dair.ai/) community. The latest version of the Google slides can be found in this GitHub repository. Our community members will continue to add more common figures and basic elements in upcoming versions. Think of this as free and open artifacts and templates which you can freely and easily download, copy, distribute, reuse and customize to your own needs. ML Visuals is now being used to power 100s of figures used by master/PhD students, papers (like this [one](https://arxiv.org/abs/2010.05113)), among other use cases.
2022-01-10 19:14:33 29.38MB 机器学习 可编辑
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作为深度学习领域的破冰之作,BP神经网络重新燃起了人们对深度学习的热情.它解决了DNN中的隐层传递中的权重值的计算问题.那么,BP算法思想是什么?它又是如何实现的呢?这就是本文的研究内容.
2022-01-10 00:15:47 40.92MB BP算法推导 期末论文 DL ML
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《 Python机器学习及实践:从零开始创造Kaggle竞赛之路(第2版)》开源数据和代码 本书的数据集,工具和代码:DIY_ML_Systems_with_Python_2nd_Edition 第二版概要: 《 Python机器学习实践(第二版)》一书适合所有对(深度)机器学习(Machine Learning),数据挖掘(Data Mining),以及自然语言处理(Natural Language Processing)的技术实践研究的初学者。 本书从零开始,以Python编程语言为基础,在不重复叙述大量数学模型与复杂编程知识的替代下,逐步将读者逐步熟悉并掌握当下最流行的(深度)机器学习,数据挖掘以及自然语言处理的开源工具库(包):Scikit学习,Google Tensorflow,Pandas,Matplotlib,NLTK,Gensim,XGBoost,OpenAI Gym等。
2022-01-08 10:44:30 295.19MB 系统开源
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