https://mfleck.cs.illinois.edu/building-blocks/index-sp2020.html
"Building Blocks for Theoretical Computer Science", Margaret M. Fleck

A #free introductory #book on Discrete Math relevant to Computer Science.
#computerscience #discretemath

https://jeffe.cs.illinois.edu/teaching/algorithms/
"Algorithms", Jeff Erickson

Another #free #book on algorithms. The book itself covers all major areas and additional online material covers many more. At the very least it features pretty funny epigraphs for its chapters. 😏

https://buttondown.email/hillelwayne/archive/why-you-should-read-data-and-reality/

Once more: we are not modeling reality, but the way information about reality is processed, by people. — Bill Kent

Эта фраза точечно объясняет, почему ООП по факту провалилось, как и примерно все остальные "методологии программирования" или проектирования.

Реальность многообразнее любой модели — это первое, что мы забываем, и оно же возвращается бумерангом чтобы хлопнуть нас по затылку в самый ответственный момент.

Ссылка на книгу внутри поста.
#free #book #modeling

https://nitter.it/Bertrand_Meyer/status/1575216523689754624

Bertrand Meyer resurrected and published for #free his 1991 #book "Introduction to the Theory of Programming Languages".

I have no idea if it's any good.

https://avehtari.github.io/ROS-Examples/

A deep #book on statistics, regression (of various kinds) and causal inference from some of the best researchers and practicioners. With a #free PDF version available (and source code of examples of course).

https://matheusfacure.github.io/python-causality-handbook/landing-page.html

#free and #opensource #book on #causalinference using Python.

ДМК Пресс выпускает перевод книги "Algorithms for Decision Making".
Оригинал доступен для скачивания бесплатно по второй ссылке. Там же можно глянуть оглавление — оно внушает уважение. 😊

#free #book #algorithms #machinelearning #reinforcementlearning

https://www.logicthrupython.org/

A #book teaching formal mathematical logic (Propositional and First-Order) essentially by building a Proof Assistant in Python. But along the way it considers important meta-theorems like Completeness and Deduction.

Both paperback and hardcover versions are available from Amazon, while chapter "drafts" are available for #free on the site as PDFs. Source code is available as well. In a ZIP file. Yep. 😏

https://fleuret.org/francois/lbdl.html
The Little Book of Deep Learning

A nice concise #free #book covering the fundamentals of #machinelearning and deep architectures, including skip connections, dropout, batch normalisation, attention, autoencoders, transformers and computer vision applications (and some other things).

Free PDFs come in two forms: mobile phone format, which is a bit annoying on a PC, and ready-to-print booklet format, which is also annoying to read on a PC. I guess they nudge you to buy a physical book, which is fair enough.

There's also a lecture course which discusses mostly the same topics, but to a greater depth, I guess. Video recordings, slides, handouts, even a virtual machine with everything installed — all available for free. François Fleuret did a great job here, for real.

https://dp.quantecon.org/

A #free #book on "Dynamic Programming" authored by Thomas J. Sargent (who got a Nobel Prise in macroeconomics) and John Stachurski.

It's a strange kind of "Dynamic Programming", the topics include:
— Fixed points and order
— Markov models
— Optimal stopping
— Markov decision processes
— State dependent discounting
— Nonlinear valuation
— Abstract dynamic programming

Examples are in #Julia and #Python

https://causalml-book.org/

#free #book on #causalinference and CausalML #machinelearning with a lot of Google Colab Notebooks in Python and R using EconML and DoubleML packages.

https://drive.google.com/file/d/17e_jZ0WwBCeIt0p1ZBF80QAsnIrDpUv6/view
"Математическая составляющая"

Сборник популярных очерков/заметок/этюдов по прикладной математике. Третья часть состоит из более развёрнутых эссе в том числе по более фундаментальным вопросам математики. Возможно, наиболее полезным является аннотированный список литературы для дальнейшего изучения и погружения.

#free #book #pdf

https://macartan.github.io/integrated_inferences/index.html
"Integrated Inferences: Causal Models for Qualitative and Mixed-Method Research"
Macartan Humphreys and Alan M. Jacobs

A #free #book providing thorough overview of Causal Models and #causalinference methods, and applying them to both qualitative and quantitative research in social sciences. Comes with an R package and examples.

More random old news (the time for a hash tag?):
https://statmodeling.stat.columbia.edu/2024/07/30/free-textbook-on-applied-regression-and-causal-inference/

Another #free #book on regression analysis with the basics of causal inference from Andrew Gelman.

Pretty comprehensive "zero to hero" course on regression including necessary math background, Bayesian treatment, generalized regression models, validation, tips and tricks.

And the source code for all the examples and everything.

https://wanminliu.github.io/Ravi_AG/Ravi_AG.html

"Foundations of Algebraic Geometry" #free #book

Includes super brief intro to Category Theory, then sheaves and presheaves, Grothendiek Schemes, their morphisms, cohomologies and all that jazz...

And another #free #book on #statistics from the list above:

https://tellingstorieswithdata.com/

It covers data search, acquisition, preparation and storage, exploratory data analysis, generalized linear models, causal inference, multilevel regression and post-stratification, visualization and reporting, and making the workflow reproducible.

Examples are in R (employing the Tidiverse), and there are questions and exercises at the end of every chapter.

https://archive.org/details/fsdesign
Practical File System Design with the Be File System

Classic examination of the subject of filesystems written by Dominic Giampaolo (initially designer of BeFS, now at Apple working on APFS).

#free #book from 1999

https://cs.uwaterloo.ca/~plragde/flaneries/LACI/
"Logic and Computation Intertwined" by Prabhakar Ragde

A #free #book on the basics and the theory of Proof Assistants that builds a simple one in Racket.

Already in the introduction the author explains the basic notions and motivations for formalized and mechanized reasoning in mathematics and software development. Good stuff.

https://sites.google.com/view/spbmath

A #free #book (a collection of essays, in English) on the life and major discoveries of many prominent mathematicians who were working in Saint Petersburg.

https://bookdown.org/aleksander_mendoza_drosik/learn-isabelle/
«Learn Mathematics and Computer Science with Isabelle»
by Aleksadner Mendoza

Unfortunately the #free #book is unfinished (and looks abandoned), thus the only Mathematics covered are the basic Set Theory, Abstract Algebra and Topology. Luckily the part on Isabelle itself is very good, covering Inductive Data Types and Predicates, Type Classes and Locales, Quotient Types, and many aspects of inner workings of Isabelle and Isabelle/HOL.