HN Books @HNBooksMonth

I made the same transition earlier in my career. One book on deep learning that meets your requirements is [0]. It’s readable, covers a broad set of modern topics, and has pragmatic tips for real use cases.

For general machine learning, there are many, many books. A good intro is [1] and a more comprehensive, reference sort of book is [2]. Frankly, by this point, even reading the documentation and user guide of scikit-learn has a fairly good mathematical presentation of many algorithms. Another good reference book is [3].

Finally, I would also recommend supplementing some of that stuff with Bayesian analysis, which can address many of the same problems, or be intermixed with machine learning algorithms, but which is important for a lot of other reasons too (MCMC sampling, hierarchical regression, small data problems). For that I would recommend [4] and [5].

Stay away from bootcamps or books or lectures that seem overly branded with “data science.” This usually means more focus on data pipeline tooling, data cleaning, shallow details about a specific software package, and side tasks like wrapping something in a webservice.

That stuff is extremely easy to learn on the job and usually needs to be tailored differently for every different project or employer, so it’s a relative waste of time unless it is the only way you can get a job.

[0]: < https://www.amazon.com/Deep-Learning-Adaptive-Computation-Ma... >

[1]: < https://www.amazon.com/Pattern-Classification-Pt-1-Richard-D... >

[2]: < https://www.amazon.com/Pattern-Recognition-Learning-Informat... >

[3]: < http://www.web.stanford.edu/~hastie/ElemStatLearn/ >

[4]: < http://www.stat.columbia.edu/~gelman/book/ >

[5]: < http://www.stat.columbia.edu/~gelman/arm/ >

⬐ soVeryTired

+1 for Gelman, but I hate Bishop's book [2]. It was an early go-to reference in the field, but there are better books out there now.

⬐ hikarudo

What do you hate about Bishop's book? I'm genuinely curious.

⬐ soVeryTired

Honestly, I don't understand the way he explains things. The maths is difficult to follow, and it just never clicks for me. Maybe he's writing for someone with a physics background or something, but I feel stupid when I read bishop.

I just read over his description of how to transform a uniform random variable into a variable with a desired distribution (p. 526). It's a fairly easy trick, but if I didn't already know it I wouldn't understand his explanation

⬐ bllguo

I'm trying to read through it and I have to agree, his math isn't that clear to me. What do you recommend?

⬐ soVeryTired

David Barber!

⬐ Iwan-Zotow

Goodfellow book [0] is available for free, http://www.deeplearningbook.org/

The most popular choices seem to be:

Machine Learning: a Probabilistic Perspective, by Murphy

http://www.cs.ubc.ca/~murphyk/MLbook/

Pattern classification, by Duda et all

http://www.amazon.com/Pattern-Classification-Pt-1-Richard-Du...

The Elements of Statistical Learning, by Hastie et all. It is free from Stanford.

http://www-stat.stanford.edu/~tibs/ElemStatLearn

Mining of Massive Datasets, free from Stanford.

http://infolab.stanford.edu/~ullman/mmds.html

Bayesian Reasoning and Machine Learning, by Barber, free available online.

http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=...

Learning from data, by Abu-Mostafa.

It comes with Caltech video lectures: http://work.caltech.edu/telecourse.html

Pattern Recognition and Machine Learning, by Bischop

http://research.microsoft.com/en-us/um/people/cmbishop/prml/

Also noteworthy

Information Theory, Inference, and Learning Algorithms, by Mackay, free.

http://www.inference.phy.cam.ac.uk/itprnn/book.html

Classification, Parameter Estimation and State Estimation, by van der Heijden.

http://prtools.org

Computer Vision: Models, Learning, and Inference, by Prince, available for free

http://www.computervisionmodels.com/

Probabilistic Graphical Models, by Koller. Has an accompanying course on Coursera.

⬐ syvlo

If you can afford it (both financially and regarding math background), Bishop is a really great choice. Almost everything you need to know is in it. I have it and just love it!

But he goes quite deep in the mathematical explanations (which is a great point, there is no better way to learn and understand) meaning you have to be willing to work on your math for this book.

⬐ simplekoala

There was post on HN of a blog post link which contained a list of all free machine learning/data mining books. Wondering, if someone can post the link to it. I am unable to find it through search.

⬐ craigching

This one?

http://christonard.com/12-free-data-mining-books/

⬐ simplekoala

yes, yes. Thanks!

⬐ gtani

or this which includes good backgrounders on lin.alg, probability and stat,

http://www.reddit.com/r/MachineLearning/comments/1jeawf/mach...

Or http://www.electronicsforu.com/newelectronics/articles/hitsc...

this is an excellent review (but doesn't cover books by Mohri, Rostamizadeh, Talwalkar and Abu-Mostafa , Magdon-Ismail, Lin: http://www.amazon.com/review/R32N9EIEOMIPQU/ref=cm_cr_pr_per...

Yep, Pattern Classification by Duda, Hart and Stork:

http://www.amazon.com/Pattern-Classification-2nd-Richard-Dud...

It is very pragmatic, including algorithms for many machine learning and artificial intelligence topics (from fitting functions for classification or regression purposes to search processes). The authors have a strong industrial background (in addition to the academic).

A great textbook as an intro is by Duda and Hart, Pattern Classification. http://www.amazon.com/Pattern-Classification-2nd-Richard-Dud.... Its pretty well written and gives a good overview of the main techniques. If you want a bit more theory, try Cherkassky and Muller, "Learning from Data".http://www.amazon.com/Learning-Data-Concepts-Theory-Methods/ Has a good overview section on statistical learning theory. And also, take WEKA and just play with it.Its nice to just check what works and what doesn't.

⬐ phektus

These are great books, thanks! Haven't heard of WEKA but it sure looks pretty nice, must like like MLDemos?

http://www.cs.waikato.ac.nz/ml/weka/