目錄

• • • • 目錄
      • 背景
      • 簡(jiǎn)介
      • 比賽
      • 相關(guān)文章

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背景

最近學(xué)習(xí)ML，NLP，DL ,發(fā)現(xiàn)有自動(dòng)寫代碼（gan 目前只能算法生成，java 有可視化的模版生成），自動(dòng)寫詩(shī)（ gan），自動(dòng)神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)（google io） 等。興奮，想想，如有一個(gè)能總結(jié)一些規(guī)律的，模式化過(guò)程，大神門經(jīng)驗(yàn)的ML框架，不用每個(gè)人從頭學(xué)習(xí)，經(jīng)驗(yàn)積累（占用大部分時(shí)間），能有更多的時(shí)間，投入到高的角度去思考創(chuàng)造（相對(duì)）。忽然遇到一篇文章，正如所想， good…(一個(gè)懶惰的碼農(nóng))

簡(jiǎn)介

What is AutoML?

Machine learning (ML) has achieved considerable successes in recent years and an ever-growing number of disciplines rely on it. However, this success crucially relies on human machine learning experts to perform the following tasks:

Preprocess the data
Select appropriate features
Select an appropriate model family
Optimize model hyperparameters
Postprocess machine learning models
Critically analyze the results obtained.

As the complexity of these tasks is often beyond non-ML-experts, the rapid growth of machine learning applications has created a demand for off-the-shelf machine learning methods that can be used easily and without expert knowledge. We call the resulting research area that targets progressive automation of machine learning AutoML.
Although it focuses on end users without expert ML knowledge, AutoML also offers new tools to machine learning experts, for example to:

Perform architecture search over deep representations
Analyse the importance of hyperparameters.

比賽

比賽名稱起止時(shí)間資源

ChaLearn Automatic Machine Learning Challenge	Dec. 8, 2014, midnight UTC—June 25, 2016, midnight UTC –	slides paper site

相關(guān)文章

• Approaching (Almost) Any Machine Learning Problem | Abhishek Thakur | Pulse | LinkedIn 原文 譯文

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