The Structure and Interpretation of Computer Programs
 Programming in Haskell - Graham Hutton
 Types and Programming Languages-Benjamin C. Pierce
If interested in why if you are an FP newbie said material is superior to SICP , read the pdf paper "The Structure and Interpretation of the Computer Science Curriculum" 
In fact what this book does is advocate multiparadigm languages (actually the author dislikes the term paradigm, multiple programming models would be more acurate), and explains in great detail how to decide which to use when, and how to mix different paradigms (ehem, models) with the very powerful technique of impedance matching.
I'm 1/3 of the way through The Little Schemer. So far, it's not taught me anything I didn't already grok from SICP. I hope it picks up!
Thank you very much for the link! :)
Opening it on my phone, this paragraph on the available ebook formats very much made my day:
> A 386 can, in theory, run Linux, Emacs, and a Scheme interpreter simultaneously, but most 386s probably can’t also run both Netscape and the necessary X Window System without prematurely introducing budding young underfunded hackers to the concept of thrashing.
The Little MLer: http://www.ccs.neu.edu/home/matthias/BTML/ which you can read with the revised Programming in SML http://www.cs.cmu.edu/~rwh/isml/book.pdf
Discrete Math & Functional Programming: https://cs.wheaton.edu/~tvandrun/dmfp/
Parallel & Sequential Algorithms uses SPARC which is similar to SML for examples in the book http://www.parallel-algorithms-book.com/
On Erlang programming books see the following list:
The Yamaha Sound Reinforcement Handbook
This is basically the soundman's bible. Sold for an aspiring live sound engineer, has much more than just advice for live sound guys - covering everything from microphones to acoustics to basic electronics to handy rules of thumb to MIDI, all written to be relentlessly pragmatic. It even has a handy appendix covering logarithms.
This isn't the book to give you the final "20%" of knowledge on anything it covers - but it will help you on your way to the first 80% a lot more quickly than most other writing on anything related to semi/pro audio, and pretty much every expert in the field is at least familiar with it, if they don't own a copy.
There seem to be plenty of people interested in music and audio around here, so hopefully someone finds the unusual reference useful.
I would recommend instead finding a well reputed mixing/mastering engineer who offers lessons and pay for some. Bring them your best mix (in the DAW on your laptop) ask them where it is wrong and how to improve it. Shouldn't take more than a couple of hours. In my case this cost around £100 (http://oood.net/mastering/about-stooodio-mastering) and helped enormously. My friends now think I'm great at "mastering" by which they mean mixing, I have nothing even approaching mastering engineer ears & I suspect Colin still wouldn't think much of my mixes either but that's why I'll happily pay him to master them if I ever have time for music again :)
I know there are online practical courses these days, in theory these could be better than a book as they would have audio/interactive examples. No idea if they actually are any good though.
Or if object X (e.g. speaker cone) moves through air with velocity v(t), what will the sound be like at position P?
 - http://www.cs.newpaltz.edu/~dosreist/
> If you want to learn how to write interpreters and compilers, and at the same time learn how Python, Python bytecode, assembly language, and dynamic typing work, this is the book for you. The only prerequisites are some experience with any programming language and a computer on which you can install Python 3 (or Python 2 if you prefer). A Raspberry Pi is not required. Included in the software package for the book is an interpreter that allows you to run ARM/Raspberry Pi assembly language programs on your Windows, Linux, or Mac OS X systems.
> If you have not yet learned Python or assembly language, so much the better. You will get the added bonus of learning Python and assembly language while you learn all about interpreters and compilers.
That sounds like a pretty sweet mix of skills to learn all at once, actually!
I can't say which is better. Nand2tetris has different approach.
These aren't really textbooks, but regardless, the Market Wizards series by Jack Schwagger is highly recommended:
While I on many levels preferred the Shreve based course, if I had to pick one for practitioners working day to day with this stuff (which I don't actually do, despite my degree), I'd definitely pick Hull.
On interest rates and yield curves, a book I would recommend is Sadr’s Interest Rates Swaps and their Derivatives . Unfortunately it is a bit dated and a lot happened since it was written. But it is a very good book to understand interest rate models, which focuses more on the intuition and practical aspects and which I think is a lot more useful to a professional.
_Animator's Survival Kit_, by Richard Williams.
_Illusion of Life_, by Frank Thomas and Ollie Johnston.
_Animation from Script to Screen_, by Shamus Culhane.
_Natural Way to Draw_, by Kimon Nicolaïdes.
_Creative Illustration_, by Andrew Loomis.
_Timing for Animation_, by Harold Whitaker and John Halas.
_Drawn to Life_, vols. 1 & 2, by Walt Stanchfield.
_Character Animation Crash Course!_, by Eric Goldberg.
_Simplified Drawing for Planning Animation, by Wayne Gilbert.
_The Noble Approach: Maurice Noble and the Zen of Animation Design_, by Tod Polson.
_Elemental Magic: The Art of Effects Animation_, vols. 1 & 2, by Joseph Gilland.
_Story Boarding Essentials_, byDavid Harland Rousseau.
_Directing the Story_, by Francis Glebas.
_Animated Storytelling_, by Liz Blazer.
Also, the war stories are both interesting and useful. Not the book for everyone; most important is to pick one you enjoy reading.
Many people - including some on HN - mistakenly equate marketing with only advertising or, more broadly, marketing communications. In truth that's only a small portion of the discipline.
Granted, I wasn’t the most dedicated student at the time, so maybe I just should read it again, but it would be helpful to know what you got out of it.
What's the competitive environment? Who is the customer (in the most specific sense)? How will the product compete against other offerings? How is it priced relative to the competition? How do you talk about the product - i.e. communicate its value? How does one think about growing the market for the product?
These are questions any product or brand need to answer, regardless of size.
That's why a good marketer should be able to work within any industry - it's pretty much: product, price, promotion and placement. This has been reinvented, rebranded, and expanded - but in reality this is the ultimate reduction you can get your marketing to.
If you went to university to study marketing, like some of us did, strategy would be put to practice in classes dedicated to it, and all at to be supported - either by theory (like Kotler's) or by research.
Copyrighting is one tool of one of the marketing mix "P"'s - Promotion.
If you want to learn about copyrighting read Ogilvy - he was very good with communication and copy.
And you're completly right - self proclaimed marketers reduce marketing to tools of one of the areas of marketing like you said : Promotion/Communication .
SEO, PPC, Content Marketing, Influencer Marketing, Social Media Marketing... the list goes on... are just tools. They serve part of 1/4 of what marketing is broadly speaking.
Now I'm not saying those shouldn't be areas of expertise - they should! But a marketing manager should build/maintain a strategy taking into account everything - not just a specific part.
If there's a need for a brand to lie then there's something wrong or missing - and that's not sustainable in the long run.
Plus, you shouldn't expose your brands to liability - for example that's why all big brands have their advertising communication evaluated by legal departments. It will not go live if legal doesn't approve it.
The Design of Everyday Things - Don Norman
Ways of Seeing - Jon Berger
Principles of Form and Design - Wucius Wong
Design Thinking: Understanding How Designers Think and Work - Nigel Cross
The book is very focused on what makes good design good, and only provides examples as illustrations. It's really more about applied psychology than design per se (in fact, Norman states in the preface that the original title was "The psychology of everyday things").
Edit: As an example in Software Engineering: His formalization of how humans interact with things (form a plan to reach a goal -> interact with the thing to advance toward said goal -> evaluate the outcome, rinse repeat) and the potential error sources on the way (distractions, unclear consequences, unclear affordances...) make it obvious why in many functional languages, the REPL is such a great tool.
I also run into "Norman doors" at least once a week and always smile when I remember the term.
Despite the emphasis on terminology, there is no better book for the fundamentals of three-dee form out there, particularly since in the digital age there is a plenitude of resources for two-dee form. If you want to know why elite architects make the decisions they do for the shape of buildings beyond their function, here is a good start.
- Mostly Harmless Econometrics by Joshua Angrist and Jorn Steffan Piscke
- Causal Inference for Statistics, Social, and Biomedical Sciences by Guido Imbens and Donald Rubin
- Data Analysis using Regression and Multilevel Models by Andrew Gelman and Jennifer Hill
(The contrast to Imbens and Rubin is crazy)
One nice thing about this list is that every recommendation must be accompanied by a few books not recommended. I think this request helps prevent well-meaning non-experts pollute the list with books from smaller reading pools.
-  Pattern Recognition and Machine Learning (Information
Science and Statistics)
-  The Elements of Statistical Learning
-  Reinforcement Learning: An Introduction by Barto and Sutton
-  The Deep Learning by Aaron Courville, Ian Goodfellow, and Yoshua Bengio
-  Neural Network Methods for Natural Language Processing (Synthesis Lectures on Human Language Technologies) by Yoav Goldberg
Then some math tid-bits:
 Introduction to Linear Algebra by Strang
-  [PDF](http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%...)
-  [amz](https://www.amazon.com/Reinforcement-Learning-Introduction-A...)
-  [site](https://www.deeplearningbook.org/)
-  [amz](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Ma...)
-  [pdf](http://incompleteideas.net/book/bookdraft2017nov5.pdf)
-  [amz](https://www.amazon.com/Language-Processing-Synthesis-Lecture...)
-  [amz](https://www.amazon.com/Introduction-Linear-Algebra-Gilbert-S...)
For the former, I would recommend Hands-On Learning with Scikit-Learn and Tensorflow
As a scientist coming to deep learning from another field, I found Courville et al to be pitched at the perfect level.
ISLR introduces you to many of the same topics in a less rigorous way. Once I was familiar with the topics and had worked through the exercises, Elements became much easier to learn from.
I'm not sure if reading Introduction will prepare you for Elements so much as it will just give you some knowledge you can use and see if it makes sense for you and what you want to do to go and (re)learn some of the math tidbits that you need for Elements.
His blog, http://www.fharrell.com, also contains interesting posts.
People seem to love this textbook - and understandably so because it's very approachable. But I really struggled with how informal the tone was, and how friendly it was. Perhaps I'd grown too accustomed to the typical theorem -> proof -> example -> problem set format.
About C++: The Design and Evolution of C++ and The C++ Programming Language, both by Bjarne Stroustrup, the creator of C++. The first one is a snapshot of his philosophy during early years of C++ and is useful to understand the motivations etc. It provides insight. The second one, after the necessary introduction to the language, shows how he uses C++, or expects to be used, which is interesting in its own way.
About algorithms: Algorithm Design by Kleinberg and Tardos. This gives the much needed insight instead of maths equations, data structure implementations or a catalog of what to apply where, which are all good, but are useless without insight.
Mechanical Engineering (ME is a large field, and I will limit to these two books.)
Stephen Timoshenko's two volume Strength of Materials is a seminal work, and still relevant, on a topic that is at the core of mechanical and civil engineering.
Shigley's Mechanical Engineering Design deals with designing machine parts, which is in a sense an applied strength of materials topic, and the book addresses that part quite effectively, though not as comprehensively as Timoshenko's. More importantly, the book gives enough motivation and insight for the design process, without which engineers would just be "design monkeys" that use latest CAE packages.
Which other Algorithms book would you recommend?
This is the book that introduced me to the wonderful world of algorithms, and I love it. The introduction chapter was a revelation and nothing like I had read before. I didn't understand everything in the first read but it made me to want to. The book is more accessible and practical than Knuth and CLRS, and more thorough than Skiena and such. Everything about this book is beautiful, not the least of which is the visualization of sort algorithms. There is also abundant tree related topics, which is quite refreshing and useful, in hindsight, because trees, other than balanced binary ones, are generally ignored in most books where graph steals the limelight.
There are two volumes that make this book, first having Parts 1 to 4, dealing with fundamentals, DS, sort and search, and the second, Part 5, dealing exclusively with graphs. I have only the first one and the opinion is based on that. There is a newer Java version, which includes the graph topics, and is about 250 pages longer than the first volume. However, based on the preview, it seems almost first 120 pages are dedicated to the Java language and OOP, unlike the C++ version which starts directly with the subject, so not sure how much of the contents are removed to make room, and how the quality differs.
Introduction to algorithms, Cormen/Leiserson/Rivest/Stein. Rigorous, detailed, thorough. Dry, technical, intimidating.
The algorithm design manual, Skiena. Friendly, informal, heuristic, insightful. Sloppy, handwavy, gappily incomplete.
They fill one another's gaps very nicely.
[EDITED to add:] I haven't read Kleinberg & Tardos, recommended above; it sounds like it may fill much the same niche as Skiena.
Kleinberg and Tardos is also friendly, informal, insightful, and even heuristic to an extent, but opposite of the others, so IMO its much better and very different than Skiena's, though it isn't a quick read before an exam or an interview sort of a book, which is what Skiena's can be described as. Browse K&T to see what I mean.
It's remarkably complete for its size. The level of detail is just enough that you can refresh or understand a topic, without drowning you in equations. The referencing could be better, but the main papers are called out.
"A Digital Signal Processing Primer" by Ken Steiglitz is a nice but rigorous intro to the subject. Written by an EE academic, it's more mathematically rigorous than Smith or Lyons.
Allen Downey's "Think DSP" is also worth a look, though its focus is more conceptual than practical, IMO.
In case you want to learn Engineering Electromagnetics, great textbook by William Hayt and John Buck
Haven't yet read any other awesome basic textbook on 'Computer Networks' like the one by Andrew S. Tanenbaum
'Digital Signal Processing' by Oppenheim and Schafer
Although these are not textbooks but among other good reads are 'Crossing the Chasm' by Geoffrey Moore, 'The Innovators Dilema' by C. M. Christensen, give amazing insights.
Millman Halkias is a staple in Indian universities but its high time it is replaced.
CA is a tough discipline to understand in my experience; the endless nuance and relativism is hard to hold in your mind and not get lost in ‘vagueness’. This is a great read to understand the field a lot better. It’s used in introductory classes at least all over Europe.
[1a, 1b, 1c] Computer Graphics, Principles and Practice Series
 Physically Based Rendering
 Real Time Rendering
Do you happen to know of any resource that provides a good mathematical foundation directed at computer graphics?
Do you have any opinion of voxel cone tracing vs raytracing?
1) The principles of chemical equilibrium, by Kenneth George Denbigh
2) Mass Transfer by Sherwood
3) Process Dynamics, Modeling, and Control by Babatunde Ogunnaike and W. Harmon Ray
4) Chemical Process Industries by Shreeves
5) An Introduction to Numerical Methods and Analysis by James F. Epperson
6) Optimization: Theory and Practice by Gordon S.G. Beveridge and Robert S. Schechter
7) Unit Operations by Maccabe and Smith
8) Advanced transport phenomena by John Charles Slattery
Admittedly these are not the best known of books (eg. Sherwoods Mass Transfer is almost out of print, in favor of Treybal) but I these are my favorites.
And the Office for National Statistics releases detailed data on suicide in the UK: https://www.ons.gov.uk/peoplepopulationandcommunity/birthsde...
For learning to cook: The Professional Chef by the Culinary Institute of America is a great book to learn from. All the recipes will need to be scaled down for home usage which is a bit of a nuisance though.
For the home cook, Essentials Of Cooking, The Elements Of Cooking, or How To Cook Everything: The Basics, are all excellent too. I couldn't decide which was the best, so I listed them all!
For Flavours: The Flavor Bible gives an easy way to look up an ingredient, and see what else would go well with it. Great for creating your own dishes!
The Flavor Thesaurus gives in-depth information about combinations of ingredients, why they work, and how best to use them.
Also recommend the Field guide to herbs and spices which gives more general information about each spice/herb than the Thesaurus. They pair well together.
The Magic Of Spice Blends is a great recipe book of various spice blends, and information about them, along with showing you how to formulate your own concoctions.
Pastries and baking: The Professional Pastry Chef: Fundamentals of Baking and Pastry by Bo Friesberg or Baking And Pastry: Mastering The Art And Craft from The Culinary Institute of America. Either or.
Confections: Chocolates and Confections by Peter Greweling.
Bread: Either Jeffrey Hamelman Bread: A Baker's book of techniques and recipes or Peter Reinhart The Bread Baker's Apprentice.
Dictionary Of Flavors. Literally a Dictionary of anything culinary related. Useful on those rare occasions.
such as? is there a community for this?
I can also recommend Modernist Cuisine, which is a sort of text book version of On Food and Cooking. Though it has a lot of industrial equipment and ingredients that aren't applicable to home cooks, it has excellent photos and diagrams that illustrate the cooking process from a biological and physical level. (Modernist Cuisine at Home isn't nearly as comprehensive, though it shirks a lot of the weird/expensive equipment and additives.)
Also Modernist Bread for breads is fantastic and comprehensive.
Dave Arnold: http://www.cookingissues.com
There is a lot of lore behind cooking, people don't always know why they do particular steps in a recipe, that's just how they were taught. It's nice to break down the fundamentals and tweak recipes with that better understanding.
For example, understanding what an emulsion is you can have a better understanding on why hollandaise sauce or mayonnaise breaks, also what acids one can replace when you don't have what a recipe calls for.
Cooking is a practice that I do several times a day, knowing the chemistry behind what I'm doing allows me more flexibility in the tools I use and the process I take.
So here you go:
The Innovators Dilema, C. M. Christensen. Just because , not really domain relevant for me but tons of interessting stuff and insights
Introduction to Materials Management, Arnold, Chapman, Clive. Covering the basics of Supply Chain Management. And I have yet to encounter a situation where the basics don't matter.
Designing and Managing the Supply Chain, Simchi-Levi, D., Simchi-Levi E., Kaminsky P.. Not just the basics of supply chain management, in essence what Amazon is doing in that regard. I have an older edition from 2005 that was just as relevant druing my Masters in 2018 as was back then, but maybe a newer edition doesn't hurt.
Logistics Engineering and Management, Blanchard. The book adding a systems engineering perspective to the above mentioned ones. A little bit weak on the actual logistics and supply chain part, which makes it even more powerfull in combination with the ones mentioned earlier. I can only recommend it for everyone working with complex long-life systems, e.g. ships, planes, industrial equipments,...
* Foundations of Statistical Natural Language Processing by Manning and Schuetze 
* Statistical Methods for Speech Recognition by Jelinek 
* Spoken Language Processing: A Guide to Theory, Algorithm, and System Development by Huang, Acero and Hon 
In particular, Statistical Methods for Speech Recognition is a book you could expect to see on the shelves of most people in the field.
Unix Network Programming - Stevens 
See the edx links in one of the threads above. Take the course; Actually DO what Gregor repeatedly says in videos and you'll find what you missed.
>> The goal would be to end with as least amount of tech debt and codebase flexible enough to stand the test of time?
I am not sure if it is possible, to have a comprehensive book with this goal in mind. It very much depends on the project size, the dependencies and your language / framework -- and even if you have that in order, there are your teammates, the process, continuous integration, milestones/deadlines and the general workflow you have to take into consideration.
I wrote my book Professional PHP  as a guide like that, but of course it's heavily skewed towards building a PHP webapp (even though most of it is also applicable to other OOP languages).
But to be honest, I wouldn't try to narrow it down to a single book. Start with the classics like clean code, code complete 2, pragmatic programmer and then work yourself towards effective java, implementing domain driven design etc. I have a list of my recommendations published here .
- Thrun et. al, Probabilistic Robotics
- Multiple View Geometry, Hartley / Zisserman
- An Invitation to 3D Vision, Ma
- Pattern Recognition and Machine Learning, Bishop
-Convex Optimization, Boyd
Photogrammetric Computer Vision: Statistics, Geometry, Orientation and Reconstruction by Wrobel and Förstner.
- "Practical Optimization" by P. E. Gill, W. Murray and M. H. Wright: a little old (1982), but provides a solid foundation
- "Convex Optimization" by S. Boyd and L. Vandenberghe: the standard for learning convex optimization (also available as a free PDF from the author's website)
- "Convex Analysis and Monotone Operator Theory in Hilbert Spaces" by H. H. Bauschke and P. L. Combettes: covers a more specialized area of numerical optimization, but the notation is beautiful (IMO) and it acts as a useful reference for recent research on, e.g., operator splitting methods
It's kind of like convex optimization is English, and nonconvex optimization is non-English. I'm not sure it's possible to write a text on non-English.
That doesn't non-convex optimization problems are unsolvable, merely that there are many different attacks that aren't necessarily coherently linked. A few common ones include:
a) convex reformulation, where possible.
b) partitioning into convex regions (used in global optimization)
c) heuristic/evolutionary approaches
d) specialized approaches for particular problem structures like integer programs, complementarity problems etc. (there are good textbooks for these)
There are a few good surveys of the landscape however. Most are journal pubs. This text  seems to be a good one.
Bozzola and Russell,
Electron Microscopy, 2nd Edition, published in 1998.
Although 20 years old in a field undergoing a revolution, the authors' method of connecting the theoretical to the (once physical, now touch screen) knobs on the instrument make it probably the best technical manual I've ever read. It's for biological EM, but the introductory chapters on the instruments are for everyone.
Transmission Electron Microscopy, materials, including high resolution, for more advanced users:
Williams and Carter, their 4 volume set, Transmission Electron Microscopy.
Christopher James, The Book of Alternative Photographic Processes.
Principles of model checking (Baier, Katoen)
It covers the basics of modeling and verifying concurrent systems against linear-time and branching-time properties. It also mentions timed automata, but those more interested in verifying real-time systems should probably check out another good introductory textbook:
Reactive Systems (Aceto, Ingólfsdóttir, Larsen, Srba)
Metallurgy: Physical Metallurgy Principles by Robert Reed-Hill
You can look at the Web Application Hacker's Handbook or the Browser Hacker's Handbook, if you want. But TTW tops them all.
This book will give you the fundamentals of application security testing.
Hacking, 2nd Edition - Introduces the foundations of memory and network exploitation
[Security Engineering](https://www.cl.cam.ac.uk/~rja14/book.html) - An overview of a huge array of info sec topics, from "E-policy" to nuclear command security.
Advanced Penetration Testing - Focuses on simulating APT attacks, using the author's penetration testing experiences to illustrate each point.
Introduction to Graph Theory by Douglas West. If you're taking a first course in graph theory, this is where you should start. There is more than enough material here for 3 semesters, and, should you finish it all, you will certainly know more than the average grad student. The only disadvantage of this book is that it's getting old. I've asked Doug when there was a new edition coming out and not gotten much of a response, so don't hold your breath. This is a solid intro to the entire field.
Topological graph theory:
Graphs, Groups, and Surfaces by Arthur T. White. Dr. White is one of the leading experts in this subfield. His previous book, Graphs of Groups on Surfaces is also recommended, if a bit pricey. Obscure and OOP mathematical monographs tend to run that way though, so, I suspect if you're interested in this book, that's not much of a problem to you.
This book is the standard textbook in topological graph theory. As I recall, the topology prerequisites are fairly minimal. For algebra, you want some basic familiarity with groups, but I don't recall anything to heavy hitting being used here. One of the main results is an outline of the proof of the Haywood map coloring theorem, which establishes the chromatic number of all orientable and non-orientable surfaces except the sphere/plane.
* Jon Kleinberg, Eva Tardos's "Algorithm Design".
* Paul Horowitz's "The Art of Electronics"
* Agner Fog's "Microarchitecture of Intel, AMD, and VIA CPUs": https://www.agner.org/optimize/microarchitecture.pdf
Not a big list, and I've got a lot of textbooks. Knuth's writing style is difficult, its the hardest read I've ever had. But Knuth hits you with the hardest examples as soon as possible, making it very "efficient" reading.
Algorithm Design is clear, concise, and practical.
Art of Electronics is one of the few books that realizes that actual chips and actual specifications are important to electronics designs. It has the unfortunate effect of going obsolete as new chips come out, but its one of the few books that digs into specification sheets and tells you what's important and how to read them.
Agner Fog's microarchitecture was more specific, up-to-date, and understandable if you read it from beginning to end. Agner Fog has a little trick: he starts with the Intel Pentium, and then describes how features were added every generation. (Branch Predicction, Pipelines, out of order, etc. etc)
- Transmission Electron Microscopy and Diffractometry of materials by Fultz and Howe
Great reference textbook on how electron microscopes are constructed, as well as general diffraction.
- Advanced Computing in Electron Microscopy by Kirkland
A comprehensive explanation of how to simulate electron diffraction patterns and electron microscopy images. Very clear explanation of the Multislice algorithm, which is actually more general than is presented in this book.
It's like a collection of all the game programming stuff they didn't teach me at school, nor at my non-game jobs. Whether you're writing an engine or just using one, I consider this book absolutely vital.
However, I don't want to disparage the tutorial nature too much, because learning to "think in Coq" has dramatically changed the way I reason about even traditional pencil and paper proofs, for the better.
That sounds intriguing, but I wonder if being more rigorous also means being much slower in completing proofs and if practicality is lost (?)
A. Zee - Quantum Field Theory in a Nutshell: this is so approachable and in the path integral formulation of QFT. Anyone who points you towards Peskin and Schroeder wants you to suffer.
Gattringer and Lang - Quantum Chromodynamics on the Lattice: essential reading for anyone learning lattice QCD.
- The Higher Infinite by Kanamori
It's probably more of a monograph than a text book, but it's among the best written monographs I've come across.
For Complex Analysis:
- Visual Complex Analysis by Needham.
It brings a lot of (needed) geometric intuition to a field that is often very easily misunderstood.
Lean software Development - Mary & Tom Poppendieck
The DevOps Handbook - Gene Kim, Jez Humble, Patrick Debois
Accelerate - Nicole Forsgren, Jez Humble, Gene Kim
Continuous Integration - Paul M. Duvall, Andrew Glover, Steve Matyas
Continuous Delivery - Jez Humble, David Farley
It's one of those books that if you read it after 10 years of industry experience it will probably be dull and obvious, but it gives novices a toolbox for solving problems.
No fluff, just lots and lots of code useful practical code samples.
2. Generative Programming (https://www.amazon.com/Generative-Programming-Methods-Tools-...)
3. PAIP (https://www.amazon.com/Paradigms-Artificial-Intelligence-Pro...)
4. Lisp In Small Pieces (https://www.amazon.com/Lisp-Small-Pieces-Christian-Queinnec/...)
5. The C Programming Language (https://www.amazon.com/Programming-Language-Dennis-M-Ritchie...)
Overcoming the Five Dysfunctions of a Team
The Wal-Mart Triumph: Inside the World’s #1 Company
The Lords of Strategy
Influence: The Psychology of Persuasion
 The Four Obsessions of an Extraordinary Executive
The Deming Management Method
The Wisdom of Teams
On Managing Yourself
The Art of Facilitation
Death by Meeting
Good Business: Leadership, Flow, and the Making of Meaning
Makers and Takers: The Rise of Finance and the Fall of American Business
How did you even begin to make this list? Out of how many books have you listed these 15? How have you applied the knowledge from these books?
Some of these are not particularly information dense, or technical guides, so why do you consider them bibles for you?
Global Shift by Peter Dicken.
Widely adopted throughout the world, this definitive text comprehensively examines how the global economy works and its effects on people and places. Peter Dicken provides a balanced yet critical analysis of globalization processes and debates.
Networking: 'TCPIP Illustrated' or it's unofficial follow up 'The Illustrated Network'
TLS: 'Bulletproof SSL' ('the Ristic book')
https://www.elsevier.com/books/book-series/handbook-of-clini... ( There are 161 volumes )
'Geometry: An Introduction'
The first one if you want a bridge to modern geometry from common university math. The second if you want to start from abstract foundations at the early-undergraduate level.
"I am not sure what book to suggest. But if your friend wants to get away from the algebra of coordinate geometry maybe a book on geometric constructions.—How to do things with a ruler and compass."
gives recommendations for more challenging or more approachable courses.
I usually suggest getting older editions of textbooks to save money, but this is one textbook where I bet you want the latest edition, because the field has been changing fast enough that there's probably some incorrect/missing information in a copy that was published even five or ten years ago. [ Edit to note: actually it looks like the latest edition was published in 2010, so it's probably quite out of date by now. :( ]
Calculus: Apostol and Spivak, take your pick
Linear Algebra: Valenza
Abstract Algebra: Artin
- Vector Calculus, Linear Algebra, and Differential Forms by the Hubbards
- Calculus on Manifolds by Spivak
If you have other recommendations, please add them! These books changed my life in the best of ways.
- Interdomain Multicast Routing: Practical Juniper Networks and Cisco Systems Solutions
- Inside Cisco IOS Software Architecture
- MPLS-Enabled Applications: Emerging Developments and New Technologies
This one's not a textbook but proved invaluable to me in learning optical networking:
- New and Updated - Everything You Always Wanted to Know About Optical Networking - https://www.youtube.com/watch?v=__wn9zXFiy8
Despite the name, that's nowhere near just an introduction, especially Part III.
Classical and Multilinear Harmonic Analysis, C. Muscalu and W. Schlag.
After reading the two volumes you will have a huge base of knowledge. The books can get quite advanced as well, containing previously unpublished results when being first printed.
Network Information Theory - El Gamal, Kim
Adaptive Wireless Communications - Bliss, Govindasamy
Wireless Communications - Goldsmith
Principles of Neural Science, Fifth Edition (Principles of Neural Science (Kandel)) 5th Edition
On Machine Learning area:
On modern software engineering and development:
On Python programming topic:
For Linux / Unix things, APUE is standard one, and pretty nice.
Am not an expert though.
The Art of Political Manipulation by William H. Riker
Get Out the Vote by Donald P. Green and Alan S. Gerber
Chemical instrumentation: A systematic Approach by Strobel. It is quite old (1989), but it is still best in class.
I would also like to know if there is a good book that covers primate biology. I had no idea it was possible to write a _good_ book about an entire order of creatures.
Introduction to Modern Statistical Mechanics - David Chandler
Physical Biology of the Cell - Phillips, Kondev, Theriot, & Garcia
For various topics, I would look at:
Introduction to Cosmology by Ryden for cosmology at the undergraduate level.
Cosmology by Weinberg.
The Exoplanet Handbook by Perryman.
An Introduction to Modern Stellar Astrophysics by Carroll & Ostlie.
Particle Astrophysics by Perkins.
Modern Statistical Methods for Astronomy by Feigelson.
Statistics, Data Mining, and Machine Learning in Astronomy by Ivezic.
I can post more in other topics if anyone is interested.