{"id":108,"date":"2023-03-10T16:51:59","date_gmt":"2023-03-10T08:51:59","guid":{"rendered":"https:\/\/my.leaf.hair\/?p=108"},"modified":"2023-03-10T16:53:52","modified_gmt":"2023-03-10T08:53:52","slug":"ml-net","status":"publish","type":"post","link":"https:\/\/my.di.cloudns.asia\/index.php\/2023\/03\/10\/108.html","title":{"rendered":"ML.NET"},"content":{"rendered":"

ML.NET<\/h1>\n

Machine Learning at Microsoft with ML.NET paper
\nhttps:\/\/arxiv.org\/pdf\/1905.05715.pdf<\/a><\/p>\n

https:\/\/github.com\/dotnet\/machinelearning<\/a><\/p>\n

High performance and accuracy<\/h2>\n

Training on ~900 MB of an Amazon review dataset, ML.NET produced a model with 93% accuracy, scikit-learn with 92%, and H2O with 85%. ML.NET took 11 minutes to train and test the model, scikit-learn took 66 minutes, and H2O took 105 minutes.<\/p>\n

NimbusML<\/h2>\n

https:\/\/github.com\/Microsoft\/NimbusML<\/a><\/p>\n

nimbusml is a Python module that provides Python bindings for ML.NET.
\n

\"\"<\/div><\/p>\n

Infer.NET<\/h2>\n

https:\/\/github.com\/dotnet\/infer<\/a>
\n\u662f\u4e00\u4e2a\u5728\u6982\u7387\u56fe\u6a21\u578b(graphical models)\u4e2d\u8fd0\u884c\u8d1d\u53f6\u65af\u63a8\u7406(Bayesian inference)\u7684\u6846\u67b6<\/p>\n

\u3000\u3000\u3000\u3000<\/p>\n

AForge.NET<\/h2>\n

https:\/\/github.com\/andrewkirillov\/AForge.NET<\/a><\/p>\n

AForge.NET\u662f\u4e00\u4e2a\u4e13\u95e8\u4e3a\u5f00\u53d1\u8005\u548c\u7814\u7a76\u8005\u57fa\u4e8eC#\u6846\u67b6\u8bbe\u8ba1\u7684\uff0c\u4ed6\u5305\u62ec\u8ba1\u7b97\u673a\u89c6\u89c9\u4e0e\u4eba\u5de5\u667a\u80fd\uff0c\u56fe\u50cf\u5904\u7406\uff0c\u795e\u7ecf\u7f51\u7edc\uff0c\u9057\u4f20\u7b97\u6cd5\uff0c\u673a\u5668\u5b66\u4e60\uff0c\u6a21\u7cca\u7cfb\u7edf\uff0c\u673a\u5668\u4eba\u63a7\u5236\u7b49\u9886\u57df\u3002\u8fd9\u4e2a\u6846\u67b6\u7531\u4e00\u7cfb\u5217\u7684\u7c7b\u5e93\u7ec4\u6210\u3002\u4e3b\u8981\u5305\u62ec\u6709\uff1a<\/p>\n

AForge.Imaging \u2014\u2014 \u4e00\u4e9b\u65e5\u5e38\u7684\u56fe\u50cf\u5904\u7406\u548c\u8fc7\u6ee4\u5668
\nAForge.Vision \u2014\u2014 \u8ba1\u7b97\u673a\u89c6\u89c9\u5e94\u7528\u7c7b\u5e93
\nAForge.Neuro \u2014\u2014 \u795e\u7ecf\u7f51\u7edc\u8ba1\u7b97\u5e93AForge.Genetic -\u8fdb\u5316\u7b97\u6cd5\u7f16\u7a0b\u5e93
\nAForge.MachineLearning \u2014\u2014 \u673a\u5668\u5b66\u4e60\u7c7b\u5e93
\nAForge.Robotics \u2014\u2014 \u63d0\u4f9b\u4e00\u4e9b\u673a\u5668\u5b66\u4e60\u7684\u5de5\u5177\u7c7b\u5e93
\nAForge.Video \u2014\u2014 \u4e00\u7cfb\u5217\u7684\u89c6\u9891\u5904\u7406\u7c7b\u5e93
\nAForge.Fuzzy \u2014\u2014 \u6a21\u7cca\u63a8\u7406\u7cfb\u7edf\u7c7b\u5e93
\nAForge.Controls\u2014\u2014 \u56fe\u50cf\uff0c\u4e09\u7ef4\uff0c\u56fe\u8868\u663e\u793a\u63a7\u4ef6<\/p>\n

Accord.NET<\/h2>\n

https:\/\/github.com\/accord-net\/framework<\/a>
\nAccord.NET Framework\u662f\u5728AForge.NET\u57fa\u7840\u4e0a\u5c01\u88c5\u548c\u8fdb\u4e00\u6b65\u5f00\u53d1\u6765\u7684\u3002\u529f\u80fd\u4e5f\u5f88\u5f3a\u5927\uff0c\u56e0\u4e3aAForge.NET\u66f4\u6ce8\u91cd\u4e0e\u4e00\u4e9b\u5e95\u5c42\u548c\u5e7f\u5ea6\uff0c\u800cAccord.NET Framework\u66f4\u6ce8\u91cd\u4e0e\u673a\u5668\u5b66\u4e60\u8fd9\u4e2a\u4e13\u4e1a\uff0c\u5728\u5176\u57fa\u7840\u4e0a\u63d0\u4f9b\u4e86\u66f4\u591a\u7edf\u8ba1\u5206\u6790\u548c\u5904\u7406\u51fd\u6570\uff0c\u5305\u62ec\u56fe\u50cf\u5904\u7406\u548c\u8ba1\u7b97\u673a\u89c6\u89c9\u7b97\u6cd5\uff0c\u6240\u4ee5\u4fa7\u91cd\u70b9\u4e0d\u540c\uff0c\u4f46\u90fd\u975e\u5e38\u6709\u7528\u3002<\/p>\n

\u7ed1\u5b9a<\/h2>\n

https:\/\/github.com\/SciSharp\/TensorFlow.NET<\/a><\/p>\n

https:\/\/github.com\/migueldeicaza\/TensorFlowSharp<\/a><\/p>\n

https:\/\/github.com\/SciSharp\/Torch.NET<\/a><\/p>\n

https:\/\/github.com\/xamarin\/TorchSharp<\/a><\/p>\n

https:\/\/github.com\/tech-quantum\/MxNet.Sharp<\/a><\/p>\n

https:\/\/github.com\/SciSharp\/Keras.NET<\/a><\/p>\n

https:\/\/github.com\/SciSharp\/SharpCV<\/a><\/p>\n

https:\/\/github.com\/shimat\/opencvsharp<\/a><\/p>\n

SciSharp\u5176\u5b83\u751f\u6001<\/h3>\n

https:\/\/github.com\/SciSharp\/NumSharp<\/a><\/p>\n

https:\/\/github.com\/SciSharp\/Plot.NET<\/a><\/p>\n

https:\/\/github.com\/SciSharp\/Pandas.NET<\/a><\/p>\n

https:\/\/github.com\/SciSharp\/PillowSharp<\/a><\/p>\n

https:\/\/github.com\/SciSharp\/Matplotlib.Net<\/a><\/p>\n

https:\/\/github.com\/SciSharp\/Gym.NET<\/a><\/p>\n

\u5176\u5b83\u673a\u5668\u5b66\u4e60\u5e93<\/h2>\n

https:\/\/github.com\/sethjuarez\/numl<\/a><\/p>\n

http:\/\/www.alglib.net\/<\/a><\/p>\n

https:\/\/github.com\/mdabros\/SharpLearning<\/a><\/p>\n

https:\/\/github.com\/jdermody\/brightwire<\/a><\/p>\n

Spark\u7ed1\u5b9a<\/h2>\n

https:\/\/github.com\/dotnet\/spark<\/a><\/p>\n

\u81ea\u52a8\u9a7e\u9a76\u4eff\u771f\u5668<\/h2>\n

https:\/\/github.com\/lgsvl\/simulator<\/a><\/p>\n

\u4eba\u8138\u8bc6\u522b<\/h2>\n

https:\/\/github.com\/takuya-takeuchi\/FaceRecognitionDotNet<\/a><\/p>\n

OpenPose\u4eba\u4f53\u59ff\u6001\u8bc6\u522b<\/h2>\n

https:\/\/github.com\/takuya-takeuchi\/OpenPoseDotNet<\/a><\/p>\n

\u795e\u7ecf\u7f51\u7edc<\/h2>\n

https:\/\/github.com\/Sergio0694\/NeuralNetwork.NET<\/a><\/p>\n

DL<\/h3>\n

https:\/\/github.com\/harujoh\/KelpNet<\/a><\/p>\n

https:\/\/github.com\/kawatan\/Merkurius<\/a><\/p>\n

RNN<\/h3>\n

https:\/\/github.com\/zhongkaifu\/RNNSharp<\/a><\/p>\n

NLP<\/h2>\n

https:\/\/github.com\/curiosity-ai\/catalyst<\/a><\/p>\n

https:\/\/github.com\/SciSharp\/CherubNLP<\/a><\/p>\n

\u5f00\u6e90.NET\u5e73\u53f0\u975e\u7efc\u5408\u7c7b<\/h2>\n

Math.NET<\/h3>\n

Math.NET<\/a>\u662f.NET\u5e73\u53f0\u4e0b\u6700\u5168\u9762\u7684\u6570\u5b66\u8ba1\u7b97\u7ec4\u4ef6\u4e4b\u4e00\uff0c\u57fa\u7840\u529f\u80fd\u975e\u5e38\u5b8c\u5584\u3002<\/p>\n

Adaboost\u7b97\u6cd5<\/h3>\n

1.https:\/\/github.com\/bgorven\/Classifier<\/a><\/p>\n

2.https:\/\/github.com\/ElmerNing\/Adaboost<\/a><\/p>\n

Apriori\u7b97\u6cd5<\/h3>\n

1.https:\/\/github.com\/Omar-Salem\/Apriori-Algorithm<\/a><\/p>\n

2.https:\/\/github.com\/simonesalvo\/apriori<\/a><\/p>\n

PageRank\u7b97\u6cd5<\/h3>\n

https:\/\/github.com\/archgold\/pagerank<\/a><\/p>\n

NativeBayes(\u6734\u7d20\u8d1d\u53f6\u65af)\u7b97\u6cd5<\/h3>\n

1.https:\/\/github.com\/Rekin\/Naive-Bayes-Classifier<\/a>
\n2.https:\/\/github.com\/ArdaXi\/Bayes.NET<\/a>
\n3.https:\/\/github.com\/amrishdeep\/Dragon<\/a>
\n4.https:\/\/github.com\/joelmartinez\/nBayes<\/a><\/p>\n

kmeans\u7b97\u6cd5<\/h3>\n

http:\/\/visualstudiomagazine.com\/articles\/2013\/12\/01\/k-means-data-clustering-using-c.aspx<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

ML.NET Machine Learning at Microsoft with ML.NET paper […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-108","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/my.di.cloudns.asia\/index.php\/wp-json\/wp\/v2\/posts\/108","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/my.di.cloudns.asia\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/my.di.cloudns.asia\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/my.di.cloudns.asia\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/my.di.cloudns.asia\/index.php\/wp-json\/wp\/v2\/comments?post=108"}],"version-history":[{"count":3,"href":"https:\/\/my.di.cloudns.asia\/index.php\/wp-json\/wp\/v2\/posts\/108\/revisions"}],"predecessor-version":[{"id":111,"href":"https:\/\/my.di.cloudns.asia\/index.php\/wp-json\/wp\/v2\/posts\/108\/revisions\/111"}],"wp:attachment":[{"href":"https:\/\/my.di.cloudns.asia\/index.php\/wp-json\/wp\/v2\/media?parent=108"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/my.di.cloudns.asia\/index.php\/wp-json\/wp\/v2\/categories?post=108"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/my.di.cloudns.asia\/index.php\/wp-json\/wp\/v2\/tags?post=108"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}