Machine Learning Yearning [pdf]

In case people are wondering, this is a draft version of the first 12 chapters of Andrew Ng's new machine learning book entitled "Machine Learning Yearning". He has a reddit thread dedicated to ongoing feedback on the book's evolving content: https://www.reddit.com/r/mlyearning/

This information was stated in his Mailchimp email update he sent out yesterday.

You can sign up for the newsletter at http://www.mlyearning.org/. Andrew will be releasing more draft chapters in the next week.

I've already read the first 12 chapters. I found it very practical for developers who have already worked with NNs. Basically the book discusses common problems and how to deal with them. Gold resource.

> Chapter 7 : with 10,000 examples, you will have a good chance of detecting an improvement of 0.1%

That's not true. Accuracy based on assuming an underlying binomial distribution has standard deviation sqrt( p (1 - p) / n ). Unless your accuracy is very near to 0 or 1, you're going to need more like a million samples to have a good chance of detecting a difference of 0.001 and be able to reject the hypothesis that it was not caused by statistical noise.

If the assumption is not an underlying binomial, can you explain why? I was hoping for more a lot more hard maths in this text.

At this stage, this book is an introduction to introduction to introduction to machine learning. It has not math/theory background. I'm curious to see the final product. However, I'm worried about the bias to NN. His plots on perf for NN vs traditional algorithms is too general and false depending on the problem.