a brief about automated machine learning

1. 1
⾃自動化機器學習
(Automated Machine Learning)
fangkuoyu@gmail.com
12/26/2017
fangkuoyu@gmail.com
03/18/2018

2. 資料科學家的過去和現在
2Source: https://www.hbrtaiwan.com/article_content_AR0002138.html
2012

3. 資料科學家的未來
3Source: https://www.kdnuggets.com/2015/05/data-scientists-automated-2025.html
KDnuggets Survey 2015

4. 資料科學家 2025 被⾃自動化取代?
4Source: https://www.kdnuggets.com/2015/05/data-scientists-automated-2025.html
KDnuggets Survey 2015

5. Google Cloud AutoML Vision
5Source: https://m.youtube.com/watch?v=GbLQE2C181U
（Automated Machine Learning）

6. Google Cloud AutoML Vision
6Source: https://m.youtube.com/watch?v=GbLQE2C181U
氣象學家辨識雲層圖像
進⾏行天氣預測或航班規劃
雲層圖像在雲端訓練

7. Google Cloud AutoML Vision
7Source: https://research.googleblog.com/2017/05/using-machine-learning-to-explore.html
氣象學家辨識雲層圖像
進⾏行天氣預測或航班規劃
• 深度學習 (deep learning)
• 轉移學習 (transfer learning)
• 強化學習 (reinforcement learning)
• ...

8. Machine Learning Landscape
8Source: http://www.techjini.com/blog/machine-learning/

9. Supervised Learning
9
Source: http://ogrisel.github.io/scikit-learn.org/sklearn-tutorial/auto_examples/tutorial/
plot_ML_flow_chart.html

10. Workflow in Supervised Learning
10
1. prepare the data
2. extract the features
3. select the model
4. select the parameters
5. train the model
6. evaluate the results
1 2
3/4/5
6
1 2

11. Workflow in Supervised Learning
11
1. prepare the data
2. extract the features
3. select the model
4. select the parameters
5. train the model
6. evaluate the results
1 2
3/4/5
6
1 2
which steps could
be automated?

12. Finding a Deep Learning Solution
12
• Selecting and Designing Network Architecture
- Multi-Layer Perceptron (MLP)
- Convolutional Neural Network (CNN)
• Selecting and Optimizing Hyperparameters
- number of layers and neurons
- objective function, activation function, optimization algorithm
- batch, epoch, learning rate, momentum, dropout, ...
• Using State-of-the-art Model and Pre-trained Weights

13. Finding a Deep Learning Solution
13
• Selecting and Designing Network Architecture
- Multi-Layer Perceptron (MLP)
- Convolutional Neural Network (CNN)
• Selecting and Optimizing Hyperparameters
- number of layers and neurons
- objective function, activation function, optimization algorithm
- batch, epoch, learning rate, momentum, dropout, ...
• Using State-of-the-art Model and Pre-trained Weights
which steps could
be automated?

14. Automated Machine Learning
14Source: https://github.com/hibayesian/awesome-automl-papers/blob/master/README.md

15. Automated Machine Learning
15Source: https://github.com/hibayesian/awesome-automl-papers/blob/master/README.md
Cloud AutoML

16. Automated Machine Learning
16Source: https://github.com/hibayesian/awesome-automl-papers/blob/master/README.md
Cloud AutoML
auto-sklearn
TPOT

17. Automated Machine Learning
17Source: https://github.com/hibayesian/awesome-automl-papers/blob/master/README.md
Cloud AutoML
auto-sklearn
TPOT
Auto-WEKA
H2O AutoML

18. Automated Machine Learning
18Source: https://github.com/hibayesian/awesome-automl-papers/blob/master/README.md
Cloud AutoML
auto-sklearn
TPOT
Auto-WEKA
DataRobot
H2O AutoML

19. 資料科學家的過去，現在，和未來
機器學習和深度學習的⾃自動化
⾃自動化學習項⺫⽬目簡介
內容回顧
19
3
2
1

20. 20
⾃自動化機器學習
(Automated Machine Learning)
THANK YOU

21. 21
⾃自動化機器學習
(Automated Machine Learning)
Case Study
RapidMiner Auto Model

22. Predict / Clusters / Outliers
22Source: https://docs.rapidminer.com/latest/studio/auto-model/

23. Select Inputs
23Source: https://docs.rapidminer.com/latest/studio/auto-model/

24. Select Models
24Source: https://docs.rapidminer.com/latest/studio/auto-model/

25. Compare Results
25Source: https://docs.rapidminer.com/latest/studio/auto-model/

26. Automated ≠ Black Boxes
26Source: https://docs.rapidminer.com/latest/studio/auto-model/