更好阅读体验,移步gitbook:https://xiaqunfeng.gitbooks.io/machine-learning-yearning/content/
NG的手稿,并没有出全,当前只出到14章。我这里边学习边翻译记录之,加深学习印象。
更新记录:
- update 2018.04.25:NG出15~19章的手稿啦,等的好辛苦,准备翻译中...
Tips:在原先的12章和13章之间新增一个章节
13 Build your first system quickly, then iterate
,原先的chapter13变为14,chapter14变为15
- update 2018.05.02:手稿 20~22 章已出
这本书的目的是教你如何做组织一个机器学习项目所需的大量的决定。 你将学习:
-
如何建立你的开发和测试集
-
基本错误分析
-
如何使用偏差和方差来决定该做什么
-
学习曲线
-
将学习算法与人类水平的表现进行比较
-
调试推理算法
-
什么时候应该和不应该使用端到端的深度学习
-
按部进行错误分析
Chapter 1、Why Machine Learning Strategy
Chapter 2、How to use this book to help your team
Chapter 3、Prerequisites and Notation
Chapter 4、Scale drives machine learning progress
Chapter 5、Your development and test sets
Chapter 6、Your dev and test sets should come from the same distribution
Chapter 7、How large do the dev/test sets need to be?
Chapter 8、Establish a single-number evaluation metric for your team to optimize
Chapter 9、Optimizingandsatisficingmetrics
Chapter 10、Having a dev set and metric speeds up iterations
Chapter 11、When to change dev/test sets and metrics
Chapter 12、Takeaways: Setting up development and test sets
Chapter 13、Build your first system quickly, then iterate
Chapter 14、Error analysis: Look at dev set examples to evaluate ideas
Chapter 15、Evaluate multiple ideas in parallel during error analysis
Chapter 16、Cleaning up mislabeled dev and test set examples
Chapter 17、 If you have a large dev set, split it into two subsets, only one of which you look at
Chapter 18、How big should the Eyeball and Blackbox dev sets be?
Chapter 19、Takeaways: Basic error analysis
Chapter 20、Bias and Variance: The two big sources of error
Chapter 21、Examples of Bias and Variance
Chapter 22、Comparing to the optimal error rate
...
当前更新到了22章,详见 draft 目录:
01-14章:Ng_MLY01-01-14.pdf
15-19章:Ng_MLY02-15-19.pdf
20-22章:Ng_MLY03-20-22.pdf