• 可以使用搜索框,搜索指定的书籍!
  • 终于有Python的书籍了,暂时没人访问😢

机器学习训练秘籍-吴恩达

AI书籍 wanyahai 3个月前 (09-28) 129次浏览 0个评论 扫描二维码

吴恩达(英语:Andrew Ng,1976 年-)是斯坦福大学计算机科学系和电气工程系的副教授,斯坦福人工智能实验室的主任。他还与达芙妮·科勒一起创建了在线教育平台 Coursera。
2011 年,吴恩达在谷歌创建了谷歌大脑项目,以通过分布式集群计算机开发超大规模的人工神经网络[2][3]。2014 年 5 月 16 日,吴恩达加入百度,负责“百度大脑”计划[4],并担任百度公司首席科学家[5][6]。2017 年 3 月 20 日,吴恩达宣布从百度辞职[7]。2017 年 12 月,吴恩达宣布成立人工智能公司 Landing.ai,他将担任公司的首席执行官。

1 Why Machine Learning Strategy
2 How to use this book to help your team
3 Prerequisites and Notation
4 Scale drives machine learning progress
5 Your development and test sets
6 Your dev and test sets should come from the same distribution
7 How large do the dev/test sets need to be?
8 Establish a single-number evaluation metric for your team to optimize
9 Optimizing and satisficing metrics
10 Having a dev set and metric speeds up iterations
11 When to change dev/test sets and metrics
12 Takeaways: Setting up development and test sets
13 Build your first system quickly, then iterate
14 Error analysis: Look at dev set examples to evaluate ideas
15 Evaluating multiple ideas in parallel during error analysis
16 Cleaning up mislabeled dev and test set examples
17 If you have a large dev set, split it into two subsets, only one of which you look at 18 How big should the Eyeball and Blackbox dev sets be?
19 Takeaways: Basic error analysis
20 Bias and Variance: The two big sources of error
21 Examples of Bias and Variance
22 Comparing to the optimal error rate
23 Addressing Bias and Variance
24 Bias vs. Variance tradeoff
25 Techniques for reducing avoidable bias
26 Error analysis on the training set
27 Techniques for reducing variance
28 Diagnosing bias and variance: Learning curves
29 Plotting training error
30 Interpreting learning curves: High bias
31 Interpreting learning curves: Other cases
32 Plotting learning curves
33 Why we compare to human-level performance
34 How to define human-level performance
35 Surpassing human-level performance
36 When you should train and test on different distributions 37 How to decide whether to use all your data
38 How to decide whether to include inconsistent data
39 Weighting data
40 Generalizing from the training set to the dev set
41 Identifying Bias, Variance, and Data Mismatch Errors 42 Addressing data mismatch
43 Artificial data synthesis
44 The Optimization Verification test
45 General form of Optimization Verification test
46 Reinforcement learning example
47 The rise of end-to-end learning
48 More end-to-end learning examples
49 Pros and cons of end-to-end learning
50 Choosing pipeline components: Data availability
51 Choosing pipeline components: Task simplicity
52 Directly learning rich outputs
53 Error Analysis by Parts
54 Beyond supervised learning: What’s next?
55 Building a superhero team – Get your teammates to read this 56 Big picture
57 Credits


本站大部分内容收集于互联网,只做学习和交流使用,版权归原作者所有。本站发布的内容若侵犯到您的权益,请联系本站处理。
喜欢 (0)
发表我的评论
取消评论
表情 贴图 加粗 删除线 居中 斜体 签到

Hi,您需要填写昵称和邮箱!

  • 昵称 (必填)
  • 邮箱 (必填)
  • 网址