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.
To be more specific, each recommendation must be accompanied by at least two other books on the subject the contributor has read and reasons why they are recommending one over the others.
If you want to know about how to build a simple computer, computer architecture, assembly language, assemblers, linkers, compilers, java, c, c++, how compilers are built, how compilers generate assembly language, how machine code are executed by the processor, how to implement a grep with regular expressions and much more. If you want to learn them fast, look for the books of this author[0]. I'm not affiliated with him. I learned many stuffs from his text books so I just like to share and recommend.
> Writing Interpreters and Compilers for the Raspberry Pi Using Python, CreateSpace Independent Publishing Platform, 2018
> 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!
Frank Harrell writes a lot of great stuff and his answers on the Cross Validated Stack Exchange site are worth just reading even if you didn't think you wanted to ask the question they reply to.
I have to disagree with The Deep Learning book. I don't find it a good book for anyone. For beginners it's too advanced/theoretical and for experienced ML scientists it's entirely too basic. I very much agree with this review on Amazon [1].
For the former, I would recommend Hands-On Learning with Scikit-Learn and Tensorflow
+1 for Elements. I started with Introduction to Statistical Learning and then graduated to Elements as I learned more and grew more confident. Those are fantastic books.
As an engineer who hadn't studied that type of math in quite a while, Elements was pretty tough and I was getting stuck a lot.
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.
If you reading Elements is difficult then I would recommend Introduction.
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.
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.
Image Processing: "Digital Image Processing", Gonzalez and Woods. Comprehensive coverage of principles, well written, practical w/ useful examples yet well grounded in signal processing and engineering principles. Complemented nicely by a version of the book rich in examples solved in MATLAB (co-author Steve Eddins).
For signals (as opposed to images), you should have a look at "Understanding Digital Signal Processing" by Richard G. Lyons (ISBN-13: 978-0131089891). I enjoyed this very much and if you grasped the contents from it, you should be able to understand audio-specific books easily.
I second Lyons. In addition, "Digital Signal Processing: A Practical Guide" by Steven Smith is also quite approachable -- immersion in calculus not required.
"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.
I can only second this. Gonzalez & Woods presents a very good overview of the material in a highly practical and understandable way. Each section contains tons of references into the more specific literature that you can follow as soon as you reached the books limits. Finally, it is a pleasure to read if you are interested in image processing and it keeps you hooked.
If you want a smaller and cheaper book on computer vision, I recommend Concise Computer Vision (Klette, Springer).
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.
Kotler's "Marketing Management" remains the most definitive text on the practice of marketing.
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.
I’m curious to know why you think it’s so good. I’ve studied marketing in the early 2000’s and Kotler’s Marketing Management was pretty much the only book we’ve read, but after college I didn’t feel I could apply much of its lessons to the companies I’ve built myself or the companies I’ve worked for. Most of Kotler’s material is based on how big traditional brands have done marketing, but that wasn’t helpful to me when I was working on early-stage tech startups.
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.
His framework for how to think about bringing a product to market is what I found most valuable.
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.
I'd argue that these fundamental questions are elaborated upon in most contemporary textbooks. In practice, however, young entrepreneurs creating business models around apps need to deal with a whole range of both general and specific issues that need to be addressed and Kotler is of limited use here.
That would also be my criticism of Kotler and Keller. It's good to learn about the marketing lingo, but it doesn't teach you how to do something concrete at a professional level (create a marketing strategy, write persuasive copy, etc.).
Well it gives you the tools and frameworks to build your own strategy, that's why it's so great.
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.
This book lays the foundation - and I've worked with FMCGs, tech, media, pharma and I've yet to find an industry where Kotler's principles do not apply.
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.
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.
I believe that's bound to the people not to the practice.
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.
I would also add "ML for the working programmer", which is, perhaps surprisingly, more about functional programming than the particularities of ML. In any case, it is a great book.
It is one of the only accessible texts on Standard ML, I'll give it that... But it goes neither deep nor wide in content. I did not find it very useful.
I am not an expert, but maybe because of that I believe that I can offer valuable advice to those who are totally new to functional programming (or feel that they are missing something), and want to get the core basics down cold without getting drowned in accidental complexity, do yourself a favor and start with edx's free moocs "How To Code" [1] [2], which are based on "How To Design Programs" [3]. After that, you will cruise through the recommended classics above.
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" [4]
I would add Concepts, Techniques, and Models of Computer Programming. It's about programming paradigms in general, and helps in contextualizing functional programming in a broader context
This book is indeed mind blowing, and after reading it I find all those FP vs OO arguments sterile. As you say, this book makes you understand that it's all a trade-off.
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 cruised through most of it. But the last three chapters really twisted my brain (continuations, the Y combinator, and the metacircular interpreter). I was familiar with the concepts, but figuring out the programs by myself was a tough exercise (I haven't read SICP though).
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.
Trust me, it will :) The last few chapters on CPS and the Y combinator are roughly a vertical segment on an otherwise nearly horizontal learning curve.
Funny, this book was considered not mathematically rigorous enough in my program but I agree, it is a bad idea to not be familiar with this book because the lingo the book uses (for example, sticky delta) is widely used in trading desks and one cannot afford to be unaware. For my program, Shreve's Stochastic Calculus for Finance (I & II) are the old & new testaments
Having used both Hull and Shreve in two different courses I'd say they're aimed at very different audiences. The course I took using Shreve didn't actually teach much about real world "options, futures, and other derivatives", but treated them more like abstract mathematical ideas and focused more on the mathematical theory used to model such contracts. While the course using Hull focused more on those contacts, not as abstract mathematical concepts, but as real things that actually exist and are actually traded by real people in the real world with all the messiness and uncertainty that can entail. Admittedly a large part of this was no doubt due to the Shreve based course being taught by the math department and the Hull based course being taught be economics department
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.
Optional types and futures are both important and useful examples of monads—in the “container” and “action” interpretations, respectively; and derivatives are the method of constructing zippers, which are an accessible example of comonads, as well as a useful method of constructing other things like parsers, so these are actually pretty solid concepts for a functional programmer—whether in Haskell or otherwise. I would totally buy that book. :)
It is true that it is, but I always thought it was underserved. It’s not particularly clear or insightful and it is very academic.
On interest rates and yield curves, a book I would recommend is Sadr’s Interest Rates Swaps and their Derivatives [1]. 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.
I studied engineering, applied maths and finance. I structured interest rate derivatives for a while. That’s the book I recommend to new joiners, even those without a stem background.
+1, this is a good one. It was the textbook for one of my grad school classes and I frequently referenced it during my postdoc.
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. :( ]
McGee has a very methodical way of discussing food. It is basically like a scientific textbook in that regard, with deeper dives into almost every topic. It is also incredibly information dense, which makes it a truly a 'bible' in my opinion. And for what it is worth, many other of the more methodical chefs on the internet think so too.
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.
I'd also recommend The science of good cooking by Guy Crosby. I found it to be a more enjoyable read than McGee's. Not as technical which was a boon for me since as a home cook there's a lot more information that can be readily skipped.
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.
Reference:
Dictionary Of Flavors. Literally a Dictionary of anything culinary related. Useful on those rare occasions.
It very comprehensive, having a section on every category of food (Dairy, eggs, meat, edible plants, fruit, etc.), the chemistry involved in cooking or preparing said foodstuff, tables of information.
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.
My favorite books contain vividly detailed descriptions of primate biology but unfortunately, none of them are safe for work.
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.
An Introduction to Modern Astrophysics by Carroll & Ostlie is the Bible, but it is quite large physically and in scope and better serves as a reference for most people.
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.
For numerical optimization, a couple good textbooks are:
- "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
Nonconvex optimization doesn't have the same depth of theoretical underpinnings or canonical body of knowledge as convex optimization so I don't imagine there's a textbook on it that would be authoritative. In the universe of optimization, convex optimization is a special case (linear optimization in turn is a special case of convex); non-convex optimization is everything else!
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 [1] seems to be a good one.
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.
It looks like there are several versions of this book. Would you recommend the 3rd edition of this book, or could I get away with an older one and buy it used?
277 comments
[ 4.9 ms ] story [ 284 ms ] threadhttps://www.lesswrong.com/posts/xg3hXCYQPJkwHyik2/the-best-t...
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.
[0] - http://www.cs.newpaltz.edu/~dosreist/
https://www.amazon.com/gp/aw/s//ref=mw_dp_a_s?ie=UTF8&i=book...
I can't say which is better. Nand2tetris has different approach.
> 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!
Everybody Poops.
My field, pooping every day.
- [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:
- [0] [PDF](http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%...)
- [0][AMZ](https://www.amazon.com/Pattern-Recognition-Learning-Informat...)
- [2] [amz](https://www.amazon.com/Reinforcement-Learning-Introduction-A...)
- [2] [site](https://www.deeplearningbook.org/)
- [3] [amz](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Ma...)
- [3] [pdf](http://incompleteideas.net/book/bookdraft2017nov5.pdf)
- [4] [amz](https://www.amazon.com/Language-Processing-Synthesis-Lecture...)
- [5] [amz](https://www.amazon.com/Introduction-Linear-Algebra-Gilbert-S...)
https://www.amazon.com/Linear-Regression-Analysis-George-Seb...
His blog, http://www.fharrell.com, also contains interesting posts.
For the former, I would recommend Hands-On Learning with Scikit-Learn and Tensorflow
[1] https://www.amazon.com/gp/customer-reviews/R1XNPL1BX5IVOM/re...
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.
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.
"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.
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.
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.
https://www.amazon.com/Marketing-Management-14th-Philip-Kotl...
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.
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.
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.
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.
[1]Overcoming the Five Dysfunctions of a Team
[2]The Wal-Mart Triumph: Inside the World’s #1 Company
[3]Guerilla Marketing
[4]The Lords of Strategy
[5]Influence: The Psychology of Persuasion
[6] The Four Obsessions of an Extraordinary Executive
[7]The Deming Management Method
[8]Creativity Inc.
[9]The Wisdom of Teams
[10]On Communication
[11]On Managing Yourself
[12]The Art of Facilitation
[13]Death by Meeting
[14]Good Business: Leadership, Flow, and the Making of Meaning
[15]Makers and Takers: The Rise of Finance and the Fall of American Business
1. https://www.amazon.com/Overcoming-Five-Dysfunctions-Team-Fac... 2. https://www.amazon.ca/Wal-Mart-Triumph-Inside-Worlds-Company... 3. https://www.amazon.com/Guerilla-Marketing-Inexpensive-Strate... 4. http://www.amazon.com/gp/product/1591397820?ref_=cm_sw_r_awd... 5. http://www.amazon.com/Influence-Psychology-Persuasion-Revise... 6. https://www.amazon.com/gp/product/B000079XXQ 7. http://www.amazon.com/Deming-Management-Method-Mary-Walton/d... 8. https://www.amazon.com/Creativity-Inc-Overcoming-Unseen-Insp... 9. https://www.amazon.com/The-Wisdom-Teams-High-Performance-Org... 10. https://www.amazon.com/Communication-featured-Necessary-Pers... 11. https://www.amazon.com/Managing-Yourself-Measure-Clayton-Chr... 12. https://www.amazon.com/Art-Facilitation-Essentials-Meetings-... 13. https://www.amazon.com/Death-Meeting-Leadership-Solving-Busi... 14. https://www.amazon.com/Good-Business-Leadership-Making-Meani... 15. https://www.amazon.com/Makers-Takers-Finance-American-Busine...
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?
[0] The Structure and Interpretation of Computer Programs
[1] The_Little_Schemer
[2] Programming in Haskell - Graham Hutton
[3] Types and Programming Languages-Benjamin C. Pierce
--- PDFS
[0](https://github.com/allenleein/brains/blob/master/Zen-of-Func...)
[1](https://github.com/allenleein/brains/blob/master/Zen-of-Func...)
[2](https://github.com/allenleein/brains/blob/master/Zen-of-Func...)
[3](https://github.com/allenleein/brains/blob/master/Zen-of-Func...)
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: http://www.aioptify.com/top-erlang-programming-books.php
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" [4]
[1] https://www.edx.org/course/how-code-simple-data-ubcx-htc1x
[2] https://www.edx.org/course/how-code-complex-data-ubcx-htc2x
[3] https://htdp.org/
[4] https://www2.ccs.neu.edu/racket/pubs/jfp2004-fffk.pdf
https://www.info.ucl.ac.be/~pvr/book.html
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.
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.
https://www.amazon.com/Trading-Exchanges-Market-Microstructu...
These aren't really textbooks, but regardless, the Market Wizards series by Jack Schwagger is highly recommended:
https://www.amazon.com/Market-Wizards-Updated-Interviews-Tra...
https://www.amazon.com/New-Market-Wizards-Conversations-Amer...
https://www.amazon.com/Hedge-Fund-Market-Wizards-Winning/dp/...
On interest rates and yield curves, a book I would recommend is Sadr’s Interest Rates Swaps and their Derivatives [1]. 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.
[1] https://www.amazon.com/Interest-Rate-Swaps-Their-Derivatives...
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. :( ]
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.
1. https://www.seriouseats.com/the-food-lab
2. https://forums.egullet.org/
Dave Arnold: http://www.cookingissues.com http://heritageradionetwork.org/series/cooking-issues/
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.
Reference: Dictionary Of Flavors. Literally a Dictionary of anything culinary related. Useful on those rare occasions.
https://www.amazon.com/Cooking-Chocolate-Essential-Recipes-T...
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.
_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.
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.
https://www.goodreads.com/book/show/705558.Universe
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.
- "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 [1] seems to be a good one.
[1] https://www.amazon.com/Nonlinear-Mixed-Integer-Optimization-...
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.
[1]https://www.amazon.com/Engine-Architecture-Third-Jason-Grego... [2]https://www.amazon.com/Real-Time-Rendering-Fourth-Tomas-Aken... [3]https://www.amazon.com/Real-Time-Collision-Detection-Interac...