python人工智能算法源码下载_Machine-Learning-in-Python 《 机器学习及实践：从零开始通往Kaggle竞赛之路》源码，提供了一些流行的 AI-NN-PR 人工智能/神经网...
文件名称: Machine-Learning-in-Python下载 收藏√ [5 4 3 2 1]开发工具: Python文件大小: 98890 KB上传时间: 2016-10-30下载次数: 0提 供 者: 启民详细说明：《Python机器学习及实践：从零开始通往Kaggle竞赛之路》源码，提供了一些流行的机器学习框架与程序库的应用实例，包括tensorflow框架，注重实战。...
文件名称: Machine-Learning-in-Python下载 收藏√ [
5 4 3 2 1 ]
开发工具: Python
文件大小: 98890 KB
上传时间: 2016-10-30
下载次数: 0
提 供 者: 启民
详细说明：《Python机器学习及实践：从零开始通往Kaggle竞赛之路》源码，提供了一些流行的机器学习框架与程序库的应用实例，包括tensorflow框架，注重实战。-Python machine learning and practice: zero to the road leading to the Kaggle contest source code, provides some popular machine learning framework and application examples, including the tensorflow framework, focusing on actual combat.
文件列表(点击判断是否您需要的文件，如果是垃圾请在下面评价投诉):
Machine Learning in Python (Scikit-learn)-(No.1\Chapter_1\.ipynb_checkpoints\Chapter_1.1-checkpoint.ipynb
...............................................\.........\..................\Chapter_1.4-checkpoint.ipynb
...............................................\.........\Chapter_1.1.ipynb
...............................................\.........\Chapter_1.4.ipynb
...............................................\........2\.ipynb_checkpoints\Chapter_2.1.1.1-checkpoint.ipynb
...............................................\.........\..................\Chapter_2.1.1.2-checkpoint.ipynb
...............................................\.........\..................\Chapter_2.1.1.3-checkpoint.ipynb
...............................................\.........\..................\Chapter_2.1.1.4-checkpoint.ipynb
...............................................\.........\..................\Chapter_2.1.1.5-checkpoint.ipynb
...............................................\.........\..................\Chapter_2.1.1.6-checkpoint.ipynb
...............................................\.........\..................\Chapter_2.1.2-checkpoint.ipynb
...............................................\.........\..................\Chapter_2.2.1.1-checkpoint.ipynb
...............................................\.........\..................\Chapter_2.2.2.1-checkpoint.ipynb
...............................................\.........\Chapter_2.1.1.1.ipynb
...............................................\.........\Chapter_2.1.1.2.ipynb
...............................................\.........\Chapter_2.1.1.3.ipynb
...............................................\.........\Chapter_2.1.1.4.ipynb
...............................................\.........\Chapter_2.1.1.5.ipynb
...............................................\.........\Chapter_2.1.1.6.ipynb
...............................................\.........\Chapter_2.1.2.ipynb
...............................................\.........\Chapter_2.2.1.1.ipynb
...............................................\.........\Chapter_2.2.2.1.ipynb
...............................................\........3\.DS_Store
...............................................\.........\.ipynb_checkpoints\Chapter_3.1.1.1-checkpoint.ipynb
...............................................\.........\..................\Chapter_3.1.1.2-checkpoint.ipynb
...............................................\.........\..................\Chapter_3.1.2-checkpoint.ipynb
...............................................\.........\..................\Chapter_3.1.4.1-checkpoint.ipynb
...............................................\.........\..................\Chapter_3.1.4.2-checkpoint.ipynb
...............................................\.........\..................\Chapter_3.2.1-checkpoint.ipynb
...............................................\.........\..................\Chapter_3.2.2-checkpoint.ipynb
...............................................\.........\..................\Chapter_3.2.3-checkpoint.ipynb
...............................................\.........\..................\Chapter_3.2.4-checkpoint.ipynb
...............................................\.........\Chapter_3.1.1.1.ipynb
...............................................\.........\Chapter_3.1.1.2.ipynb
...............................................\.........\Chapter_3.1.2.ipynb
...............................................\.........\Chapter_3.1.4.1.ipynb
...............................................\.........\Chapter_3.1.4.2.ipynb
...............................................\.........\Chapter_3.2.1.ipynb
...............................................\.........\Chapter_3.2.2.ipynb
...............................................\.........\Chapter_3.2.3.ipynb
...............................................\.........\Chapter_3.2.4.ipynb
...............................................\.........\MLLab_install.sh
...............................................\........4\.ipynb_checkpoints\Chapter_4.2-checkpoint.ipynb
...............................................\.........\..................\Chapter_4.3-checkpoint.ipynb
...............................................\.........\..................\Chapter_4.4-checkpoint.ipynb
...............................................\.........\Chapter_4.2.ipynb
...............................................\.........\Chapter_4.3.ipynb
...............................................\.........\Chapter_4.4.ipynb
...............................................\Datasets\Breast-Cancer\breast-cancer-test.csv
...............................................\........\.............\breast-cancer-train.csv
...............................................\........\IMDB\300features_20minwords_10context
...............................................\........\....\labeledTrainData.tsv
...............................................\........\....\submission_count.csv
...............................................\........\....\submission_tfidf.csv
...............................................\........\....\submission_w2v.csv
...............................................\........\....\testData.tsv
...............................................\........\....\unlabeledTrainData.tsv
...............................................\........\MNIST\conv_submission.csv
...............................................\........\.....\dnn_submission.csv
...............................................\........\.....\linear_submission.csv
...............................................\........\.....\test.csv
...............................................\........\.....\train.csv
...............................................\........\Titanic\rfc_submission.csv
...............................................\........\.......\test.csv
...............................................\........\.......\train.csv
...............................................\........\.......\xgbc_best_submission.csv
...............................................\........\.......\xgbc_submission.csv
...............................................\Chapter_1\.ipynb_checkpoints
...............................................\........2\.ipynb_checkpoints
...............................................\........3\.ipynb_checkpoints
...............................................\........4\.ipynb_checkpoints
...............................................\Datasets\Breast-Cancer
...............................................\........\IMDB
...............................................\........\MNIST
...............................................\........\Titanic
...............................................\Chapter_1
...............................................\Chapter_2
...............................................\Chapter_3
...............................................\Chapter_4
...............................................\Datasets
Machine Learning in Python (Scikit-learn)-(No.1
输入关键字，在本站266万海量源码库中尽情搜索：
帮助
魔乐社区（Modelers.cn) 是一个中立、公益的人工智能社区，提供人工智能工具、模型、数据的托管、展示与应用协同服务，为人工智能开发及爱好者搭建开放的学习交流平台。社区通过理事会方式运作，由全产业链共同建设、共同运营、共同享有，推动国产AI生态繁荣发展。
更多推荐
0
0- 0
已为社区贡献3条内容
所有评论(0)