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Machine Learning Yearning

4.31  ·  Rating details ·  343 ratings  ·  44 reviews
AI, machine learning, and deep learning are transforming numerous industries. But building a machine learning system requires that you make practical decisions:

Should you collect more training data?
Should you use end-to-end deep learning?
How do you deal with your training set not matching your test set?
and many more.

Historically, the only way to learn how to make thes
ebook, 118 pages
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Average rating 4.31  · 
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Dec 11, 2018 rated it it was amazing
My favorite course from the Deep Learning Specialization on Coursera was "Structuring Machine Learning Knowledge" because it contained practical insights that were difficult to find elsewhere. Deep Learning Yearning contains much of the same information as that course, much of it expounded upon, and in a format that is easy to share with teammates and collaborators. As such, I think it is a valuable contribution to the field and deserves a spot on practitioner's bookshelves. Andrew Ng's writing ...more
Precia Carraway
May 31, 2018 rated it really liked it
Andrew Ng is giving practical advice to the ML engineer through his experience at Google Brian, Baidu and teaching (Stanford and Coursera). It's a light technical book, giving succinct technical advice from someone being in the field...rules of thumbs, tricks, layman advice from lots of practice, trial and error. ...more
Eddie Chen
Jan 01, 2021 rated it it was amazing
classic ML reference book from Andrew Ng, geared towards ML practitioners
Apr 16, 2018 rated it it was amazing
Read the draft chapters and I can't wait for the final version to be released. ...more
Jul 06, 2019 rated it really liked it
Shelves: 2019_read
Lightweight, technical and practical. Highly recommended.
Satyabrata Mishra
Jun 22, 2020 rated it really liked it
4 stars because it is a rehashed version of the ' Machine Learning Pipeline' Course on Coursera ...more
Paweł Cisło
Jan 24, 2020 rated it really liked it  ·  review of another edition
Shelves: _owned, data_science
The e-book is full of high-level descriptions, which should satisfy AI project managers who would like to have a quick introduction into the basics of ML. For the more technical readers, I would instead propose to have a look into Andrew's ML Coursera material.

Overall, the material introduces lots of practical approaches, mainly for diagnosing errors in the ML systems. Still, I think that some of the presented concepts, such as "eyeball dev set" and "unavoidable bias" are not that common in the
Dec 15, 2016 rated it liked it
I've just read the "Takeaways" page in the end and it's enough for now.
The book deals with classical machine learning and not convolutional neural networks, so I'll get to read it fully someday later when it's out of beta and I have time to study it and Bishop's Machine learning book.
André Pinto
Apr 22, 2021 rated it really liked it
Interesting book for machine learning practitioners. Gives some general guidelines without being too technical. However if you are interested in learning about, or getting started on Machine Learning and/or Deep Learning, this is not the book for it.
Jan 09, 2020 rated it really liked it
More for those currently on the practical side creating something. Bits and pieces also useful for those looking to understand what AI is better.
Ravi Teja
Oct 26, 2019 rated it liked it
Shelves: acad
This can be a good book to always have on the desk, why? It lists down most of the quick diagnosing things in one place, so we can look at it in the moment of confusion. If you're an ML practitioner it doesn't tell anything that is new related to algorithms or anything like that but it helps one structure his/her ml project in a coherent manner. And small bite sized chapters, much like his lectures help us to search exactly for the problem we are facing and take an action that gives the best ret ...more
Jun 17, 2020 rated it it was amazing
* A Hook
What happens when you try to make the Deep learning (DL) textbook into a pocket version?

* Essential Book Information
One-page recipes for the Machine and Deep learning practitioner, a reference text.

* Your Praise and Critique
Ideal for graduate entry level.
It's not a stylistic book. Ng probably wrote it in a couple of days, yet it has well-thought illustrative examples.

* Your Recommendation
This is the first book on ML/DL I recommend before reading the DL textbook.

* Your Rating
Nicole Nair
Jan 07, 2022 rated it it was amazing
This is a review of a draft of this book.

This book provides design patterns for error analysis of machine learning models.

I wouldn't rely on this book for machine learning theory of course, but most machine learning courses/books focus heavily on theory & standard algorithm implementations, neglecting the bread and butter of ML i. e. error analysis. So this book is a good supplement to the other resources.

Will be keeping this book close at hand for future reference!

(Less important, but this book
Ruta Remutyte
Dec 29, 2020 rated it liked it
I fail to see who is the target reader here and what is the goal of this book.

• Are you new to ML? This book is not for you.
• Did you complete one of Andrew Ng’s online courses? The content is pretty much the same (even the examples are the same) so you won’t learn anything new.
• Are you experienced ML practitioner? It lists down quick diagnosis ways so I guess it could serve as a cheat sheet but that’s pretty much it.

I am confused whether this is an MVP of the future book but I can’t say I f
Kapil Dua
Jan 25, 2019 rated it it was amazing
Shelves: ai-ml
Hallmark of Andrew's teachings is the ability to present the most complex concepts in the simplest possible terms without losing the essence of the lesson. This book is an exemplar of that ability. This book is like a timeless cookbook for those designing ML systems from the ground up. I would highly recommend this book for those looking for clarity in designing ML solutions. ...more
Eryk Banatt
Apr 26, 2019 rated it it was ok
Short, extremely rudimentary book which is basically just enough best practice for (I guess) startups to figure out how to develop ML solutions without really understanding them. Overall pretty disappointed by this considering it’s by Andrew Ng whose coursera content I think is excellent, but I suppose it is just a draft after all
Lara Thompson
Jan 03, 2019 rated it really liked it
Shelves: technical
Worth the very short time it took to read. It's all about workflow, debugging errors in machine learning pipelines, designing those pipelines, debugging your model vs dataset choices. It could be a lot less repetitive though, hence the lost star in my rating. ...more
Edgar Guevara
Mar 03, 2019 rated it it was amazing
Brevity is the highest quality of this book. Very sparse on the technical side of machine learning, however, straight to the point. Andrew Ng gives all the important tips on troubleshooting a machine learning system in real life. In summary, a must read, after taking Ng's machine learning MOOC. ...more
Vivek Mishra
Dec 01, 2019 rated it really liked it
Read the draft sent by Andrew Ng, hopefully he will complete the book very soon. I learnt a lot from his Stanford videos lectures and this book gives quite broad view of ML, can't wait to see complete book. ...more
Felix U-O
Apr 08, 2020 rated it liked it
It was OK with nice practical examples and some good principles to inform anyone seeking to implement ML methods. Somehow it felt a little rushed, lacking a good structure. But it was free, so it was an interesting little read.
Aug 23, 2018 rated it liked it
Shelves: 2018, ai
Rating is for the draft version of the book.
Oct 02, 2018 rated it liked it
Shelves: computing
Straight forward practical advice, not immensely illuminating
Oct 02, 2018 rated it really liked it
A very good summary of machine learning best practices from one of the most respected machine learning researchers and instructors in the world.
Nov 04, 2018 rated it it was amazing
What excels about this book is the fact that it focuses and provides tips on actual ML problems a developer could encounter in a production and live environment.
Setia Budi
Dec 17, 2018 rated it it was amazing
Shelves: machine-learning
This is probably one of the best introductory to machine learning and deep learning :)
Taras Petrytsyn
Jan 18, 2019 rated it it was amazing
Interesting book with fresh look on some problem from one of the guru of deep learning. It's not a detailed tutorial, rather cookbook for peoples who already have some basic experience. ...more
Jun 12, 2019 rated it really liked it
Quick and concrete advice on how to carry out a machine learning project, and what systematic approaches one can take to improve one’s models. Helpful!
Jun 22, 2019 rated it it was amazing
Breaking down the content into small chapters makes it a joy to read.
Sebastian Conrady
Oct 31, 2019 rated it it was amazing
Must read for every Data Scientist and even more for Project Managers!
Natu Lauchande
Dec 30, 2019 rated it really liked it
Quick read in one go. Easy read and tons of good and practical tips.
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