| English | Chinese |
|---|---|
| ad-hoc, ad hoc (fix/method) | 特设的（修改/方法） |
| training (dataset) | 训练（集） n. |
| evaluation (dataset) | 验证（集） n. |
| testing (dataset) | 测试（集） n. |
| train | 训练 v. |
| evaluate | 验证 v. |
| test | 测试 v. |
| 试 v. | |
“if you plan to use deep learning extensively (>150 hrs/mo), building your own deep learning workstation might be the right move.” [medium]
ref:
See simple DEF & code in 邱锡鹏 教授 2020 神经网络与深度学习.
See also:
DSML (Kroese, Botev et.al. - Chapman Press 2019) Data Science and Machine Learning: Mathematical and Statistical Methods. With public datasets, code and pdf online.
NNDL (Michael Nielsen 2016 邱锡鹏（译） 2020.06) Keywords: CNN, RNN, Attention, Gaussian Mix., RBM, DBN, GAN, RL, TF, PyTorch: equation, pseudocode, exercise … 公开了所有代码等内容的（中文）电子书。 PDF, PPT, code, exercise & solution, etc.
MLAPP (Kevin Murphy), Machine Learning - A Probablistic Perspective, is more comprehensive, insightful and interesting, and contains more “real” examples/problems. However, the presents are kinda out of order, which can be difficult to follow for a first book.
Read also:
Most terminologies have been defined well, except “data mining” as the biggest concept.
...See also:
DL-theory > ## cnn visualization/comprehensibility
LIME (万金油), Tree interpreter, etc.
凭什么相信你，我的CNN模型？关于CNN模型可解释性的思考 inc.:CAM, Grad-CAM, Lime.
Book by Christoph Molnar: Interpretable Machine Learning – A Guide for Making Black Box Models Explainable (GitHub), (CN)
All-in-one: SHAP.
ref:
https://medium.com/@Zelros/a-brief-history-of-machine-learning-models-explainability-f1c3301be9dc
Ref:
en towardsdatascience.com
cn weixin
SHAP for ML.
See also: ML books.
This blog collects some useful materials for non-theory learners like engineers.
Book: python-machine-learning-book
code on github
ML from scratch (py)
Erik Linder-Norén, Stockholm
Machine learning, in numpy (so, also scratch, but a lot Neural Nets & RL)
David Bourgin, CA
7 Types of Regression Techniques you should know (modern regressions)
analyticsvidhya
evernote backup
GitHub标星1.3k！一款功能强大的特征选择工具 2019.11
Causality analysis: MS dowhy, Causalinference in py (inactive), CausalInference in Julia, IBM causallib etc.
Machine Learning Logistics, by Ted Dunning, Ellen Friedman @O’Reilly full text. Keywords: Rendezvous Architecture; Data Science in Production; Model Management from the Front Lines.
...This is a summary of KD-nuggets blogs: here and here. Pictures are modified for my own notes.
Possible framework 1: Hadoop + Spark + Python + scikit.
Possible framework 2: SQL+ Excel + Tableau.
Try NOT use: RapidMiner, KNIME (whatever situation).
Conclusion 1: Big_Data & DL are positively related.
Conclusion 2: Scala is designed/dedicated for Big_Data & DL.