Found in 3 comments on Hacker News
epgui · 2021-12-13 · Original thread
I spend a lot of time sniffing them out.

One thing I'll do once in a while is visit a university bookstore to get a sense of what's being used in classrooms. I ended up with a lot of my law textbooks like that. In law, I've really loved:

- Canadian Administrative Law (Guy Régimbald):

- Tort Law (Horsey & Rackley):

- Handbook of Canadian Higher Education Law (Shanahan, Nilso & Broshko):

I've been on a maths, stats and ML/AI binge in the last year, and so far my top picks are:

- Calculus (Stewart):

- Linear Algebra Done Right (Sheldon Axler):

- An Illustrative Guide to Multivariable and Vector Calculus (Miklavcic):

- Probability: A Graduate Course (Allan Gut):

- Statistical Rethinking (McElreath):

- An Introduction to Statistical Learning (James, Witten, Hastie & Tibshirani):

- Regression and Other Stories (Gelman, Hill & Vehtari):

- Data Analysis Using Regression and Multilevel/Hierarchical Models (Gelman & Hill):

- Statistical and Data Analysis for Financial Engineering (Ruppert & Matteson):

- Reinforcement Learning (Sutton & Barto):

- Deep Learning (Godfellow, Bengio & Courville):

- Networks (Mark Newman):

I have a ton of textbooks in my wishlist that seem noteworthy and interesting, but that I haven't gotten the chance to read yet: no promises, but you might find something in there

My area of expertise is biochemistry, life science, immunology, cell bio... So if anyone wants good recommendations in these topics I have plenty! :)

Yadi · 2018-10-01 · Original thread
In machine learning, hands down these are some of the best related textbooks:

- [0] Pattern Recognition and Machine Learning (Information Science and Statistics)

and also:

- [1] The Elements of Statistical Learning

- [2] Reinforcement Learning: An Introduction by Barto and Sutton

- [3] The Deep Learning by Aaron Courville, Ian Goodfellow, and Yoshua Bengio

- [4] Neural Network Methods for Natural Language Processing (Synthesis Lectures on Human Language Technologies) by Yoav Goldberg

Then some math tid-bits:

[5] Introduction to Linear Algebra by Strang

----------- links:

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- [0][AMZ](

- [2] [amz](

- [2] [site](

- [3] [amz](

- [3] [pdf](

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- [5] [amz](

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.

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