Post what you think is the definitive text on a field of your choice (textbook, paper, whatever)
Rules: if you disagree with someone's pick for their field, offer an alternative.
Starting with Introductory E&M
overcited but great exposition for applications, all you need is like, calculus 1, linear algebra and stats and it gives you all the intuition you will ever need
its a totally new perspective, you will seee the world differently after reading this
although if you want to study information theory for the sake of it you will need to do a lot more work
go home ass hat
rudin sacrifices lucidity for trite concise proofs
the exercises are hard and offer in return nothing other than a waste of time and a sore brain, there are less painful ways to acquire practice
its a book people claim to have read for 'bragging rights'
discard everyone who claims to have worked through rudin
dont bother, read something with decent exposition like bartle, then go to folland for a treatment of the non-training-wheels theory of integration
literally anyone but rudin
Easy books like Griffiths make people feel good because it makes the reader believe they understand the subject. Only once they try to read higher level books they will realize that they haven't actually understood shit and fail badly.
cool... a philosophy major!
Hoping this for FEM, just picked it up
>he fell for the computer science meme
no this is actually a good book
i think textbook threads are the best threads on /sci/ even with all of the memes and shitposting
>the exercises are hard
I don't think you belong in mathematics my friend.
Rudin is an astounding book in beauty and conciseness, every reputable mathematician I've ever met has said that. If you don't understand it, I invite you to study harder instead of hating on people who do.
>Writing a book about compilers that targets kids
topkek, I can appreciate the believes of the author but it is clear now that any kid who touches programming will just be doing shitty 2D videogames.
[scispoiler]talking from experience[/scispoiler]
The core of any biophysics PhD program curriculum is statistical thermodynamics.
God King of Developmental Biology texts
missing only problem sets, otherwise it would be the only book you'd ever need.
(Stinson has dank problem sets btw)
>It makes physical predictions, such as:
>the existence of supersymmetric particles
That's supersymmetry not string theory. Supersymmetry was woven into string theory but the predictions of extra particles wasn't a string theoretic prediction. Looks one more guy who doesn't know what he's talking about.
>b..but he has a Ph.D
So? Doesn't mean he knows shit about string theory.
Not who you were replying to, and I agree with you more or less.
If you are not analytically inclined though, there are better books. I knew I had no interest in analysis by the time I was in grad school, so I just used Apostol and some other measure theory book. I looked at both Rudins and Royden and would have hated using any of those as a course text. I liked the thorough explanations and handholding. Maybe I didn't learn analysis as well, but I don't care about analysis past passing a class or two and a qual.
Pretty cover for an overpriced piece of shit to be honest.
A text should never try to cover calculus, statistical mechanics and all those applied topics so superficially in on book.
I would recommend alternatives, but there are no good definitive texts in the field.
I feel you can't do a best book on EM. It can be written down, approached and used in so many different languages - you have to make a choice and it's necessarily offensive.
The problem is that string theory is an seemingly infinite field and this is just the intro to the entry ideas
Rudin is a worse text for the analytically inclined as well. Getting through Rudin with a solid mechanical understanding of the material, just to pass a course, say, is doable.
Building a solid intuition for the basic ideas of analysis (which you absolutely need if analysis is important to you) requires such heavy supplementing that you might as well just use another book, because Rudin gives you absolutely nothing in the form of guidance to figure out how to start thinking about these topics and what they mean.
Carothers is an analysis book that happens to be a great example of what I'm talking about with explanations. It's what I used on my own after my uni used Rudin and I really wish I had seen it first.
ST is so fucked, they organized a conference with philosophers to discuss how to proceed.
>Fundamental physics faces a problem, Gross explained — one dire enough to call for outsiders’ perspectives. “I’m not sure that we don’t need each other at this point in time,” he said.
>It was the opening session of a three-day workshop, held in a Romanesque-style lecture hall at Ludwig Maximilian University (LMU Munich) one year after George Ellis and Joe Silk, two white-haired physicists now sitting in the front row, called for such a conference in an incendiary opinion piece in Nature. One hundred attendees had descended on a land with a celebrated tradition in both physics and the philosophy of science to wage what Ellis and Silk declared a “battle for the heart and soul of physics.”
>The crisis, as Ellis and Silk tell it, is the wildly speculative nature of modern physics theories, which they say reflects a dangerous departure from the scientific method. Many of today’s theorists — chief among them the proponents of string theory and the multiverse hypothesis — appear convinced of their ideas on the grounds that they are beautiful or logically compelling, despite the impossibility of testing them. Ellis and Silk accused these theorists of “moving the goalposts” of science and blurring the line between physics and pseudoscience. “The imprimatur of science should be awarded only to a theory that is testable,” Ellis and Silk wrote, thereby disqualifying most of the leading theories of the past 40 years. “Only then can we defend science from attack.”
There is but one definitive text/paper in information theory
Italian edition master race
You'll have to complement this with some other book for applications, but if you are mathematically inclined the theorical exposition here is sublime. It's a good introduction to formalism, and beautifully leads the way to abstract algebra
you will not find a number theory text from the last hundred years that doesn't cite this
If you're a third or fourth year undergrad, have some prior experience with basic set theory and a bit of algebra, then maybe
trying to learn set theory for the first time from Jech would be a bit like trying to learn basic calculus using Rudin
Some introductory books (I used them during my Msc)
Statistical mechanics : Introduction to modern statistical mechanics by David Chandler
Quantum mechanics : Introduction to Quantum Mechanics by David J Griffits
"Astrophysics" : The Physics of Astrophysics Vol 1 and Vol 2 by Frank H Shu
GR/Cosmology : Gravitation and Cosmology: Principles and Applications of the General Theory of Relativity by Steven Weinberg
Particle Physics : Techniques for Nuclear and Particle Physics Experiments by William R Leo
Yo I taught myself from Weinberg's book and after reading through MTW/online relativity problem book I think it's pretty lacking. It's a good reference tool and I still mastered quite a bit of index gymnastics/manipulation from Weinberg but severely lacked a good deal of physical discussion until the latter parts of the book. That said, MTW is pretty damn wordy.
Griffiths also can't hurt as a first pass, but found a great deal of tedium in some differential equations versus a more abstract approach to QM.
I only finished up through around chapter 6 or so, but Lee's Intro. to Smooth Manifolds was a great read for the basic foundation of diff. geo.
Exercises and problems were good, decent development of material. Really helped me get an idea of the behind-the-scenes in courses like GR/field theory.
Does it relate to linear/nonlinear/integer programming in any way or is it a totally different kind of optimization? Could you give a few simple examples of real world problems you can solve with the mathematical techniques that book teaches?
Only real prereqs are organic chemistry, though if you're weak on chemical solutions thermodynamics and analytical/quantum chemistry everything past the first 10 synthesis chapters will be largely inaccessible. The last few chapters also assume a very decent background in material science and engineering and the very last chapters requires some knowledge of general EE (microelectronics) at a second year level.
It's a very multidisciplinary field.
Their service is also garbage.
I asked them about missing chapters in an IE edition that they not warn they were going to remove (they also removed the preface and very important appendices) asking if we could get electronic copies and if the print was intentional or it's a misprint.
They fucking ignored me for weeks then tried to blame the bookstore of all people I eventually found the missing chapters I needed available on the website by the author.
The paper quality was also so fucking shit the book fell apart not 2 months after buying a brand new softcover.
Fuck Pearson. Boycott them and tell your lecturers not to prescribe their garbage.
This is *the* algebra text. Obviously it's not the be-all and end-all of the subject, but if your field requires more than a passing understanding of algebra then you need to know just about everything in this book like the back of your hand.
Leroy G. Wade is a fucking god.
not really when I was 12,I was programming low level stuff because I wasn't creative enough to make a game. What I could do was try to understand how computers really worked and do low level stuff
Pharm. Polish translation is great, don't know if it's available in English, though. Great book, but antibiotics part sucks.
For anyone new to /sci/, these are the quintessential meme books that we recommend here.
Smooth Manifolds by Lee
Rudin only, R&C Analysis or Principles, depending on mood
Autism by Lang
I am looking for a book about Mathematial physics, focusing on Complex variables, PDE and Fourier transform (basically, starting after multivariate calculus and ODE). What is the /sci/ recommended?
Alternatively, which of these that are included in the torrent in /sci/ wiki do you know and recommend (quite a list):
A Course in Modern Mathematical Physics - Groups, Hilbert Spaces and Diff. Geom. - P. Szekeres
A Guided Tour of Mathematical Physics - Roel Snieder
Applied Mathematical Methods in Theoretical Physics - Masujima M.
Calculus Of Variations With Applications To Physics & Engineering - R. Weinstock
Equations of Mathematical Physics - Bitsadze A.V.
From calculus to chaos - Acheson
Homological Methods in Equations of Mathematical Physics-J.Krasil'schchik
Iintroduction to Groups, Invariants and Particles - F. Kirk
Math methods in physics and engineering with Mathematica - F. Cap
Mathematical Methods for Physicists - a Concise Introduction - T. Chow
Mathematical methods for physics and engineering - Riley, Hobson
Mathematical Methods of Classical Mechanics, 2nd ed. - V.I. Arnold
Mathematical Tools for Physics - J. Nearing
Methods of Modern Mathematical Physics Vol 1 - Functional Analysis 2nd. ed. - M. Reed
Methods of Modern Mathematical Physics Vol 2 - Fourier Analysis, Self Adjointness - 2nd ed., - M. Reed
Methods of Modern Mathematical Physics Vol 3 - Scattering Theory - M. Reed
Methods of Modern Mathematical Physics Vol 4 - Analysis of Operators - M. Reed
The Fourier Transform And Its Applications - Bracewell
The Mathematical Beauty of Physics - World Scientific -
Topics in Mathematical Physics - Victor Palamodov
Hassani - Mathematical Physics: A Modern Introduction to Its Foundations
Applied Partial Differential Equations: With Fourier Series and Boundary Value Problems by Haberman
Partial Differential Equation: An Introduction by Strauss
Partial Differential Equations by Fritz John
Fourier Series by Tolstov
Fourier Analysis and Its Applications by Folland
Fourier Analysis: An Introduction by Stein & Shakarchi
Fundamentals of Complex Analysis: With Applications to Engineering and Science by Saff and Snider
Visual Complex Analysis by Needham
Complex Analysis by Stein & Shakarchi
Those are introductory texts for all the theory that is behind compilers. Personally I think both volumes of "Parsing Theory" (pic related) are the best books about parsing theory... obviously.
I consider that book as one of the worst on the topic: lacks definitions and explain concepts using others that haven't been explained yet. I prefer "Automata, languages and machines" (volume A and B) by Samuel Eilenberg. A book using this theory and very interesting is "Algorithms on strings" by Maxime Crochemore.
These are the only two textbooks I have every used significantly. Nesse could even help a bio major understand optical mineralogy and his birefringence charts don't suck massive dick.
I read this as a hobby, has anyone else read it? Is it good?
Is Hartle's Gravity a good book for self study or should I get Schutz?
>No, it's not. It's about memorizing shit, biofag.
I would attempt a legitimate response, but then I remember that this is /sci/, where 18-year-olds who just passed Calc I and first-semester Gen Physics (read: Classical Mechanics) think that now they're on their path to getting PhDs in high energy particle theory from Princeton as they quote Ernest Rutherford while looking down on the rest of us for "stamp collecting".
>Aren't biophysics PhDs mostly coming from a physics/engineering/chemistry background?
>Yeah, you usually apply to a Physics PhD program then move over into it. Unless specifically biophysics programs are becoming more popular in recent years.
Absolutely. Biophysics PhD programs, specifically, are mainly filled by physics and math majors, followed by chemistry majors and engineering majors. In my experiences being around four programs, and interviews at a few more, from undergrad to being a lab tech at a few schools prior to starting my PhD, I would say that less than 1 out of 12 or so biophysics PhD students has an undergrad degree in biology.
>A text should never try to cover calculus, statistical mechanics and all those applied topics so superficially in on book.
Don't disagree with the point on calculus.
I think what makes the book standard in biophysics PhD programs is Dill's explanation of his extension of statistical mechanics to study protein folding.
All CS books are written at the high school level or below. How else do you think those subhumans pass their courses?
The mathematics level in CLRS is appallingly low. It's an algorithms book for high school or middle school kids.
Get a real book if you want to be able to analyze algorithms.
Is this book good for building intuition on the subject or is it just a meme?
What's a good textbook for discrete math? inb4 Concrete Math, that's analytic combinatorics. Great but not what I'm looking for. inb4 Rosen. Rosen spends too much time harping on proofs as though I'm a child who hasn't already worked through a book like The Book of Proof by Hammack (excellent book btw).
I'll take a general combinatorics book too, if all "discrete math" books are actually cancer.
Discrete math books are almost universally terrible. Just read a separate book for each topic.
A Transition to Advanced Mathematics by Smith, Eggen, and Andre
Conjecture and Proof by Laczkovich (Supplement)
Introduction to Probability by Bertsekas and Tsitsiklis
Probability in Electrical Engineering & Computer Science: An Application-Driven Course by Walrand (Supplement)
All of Statistics: A Concise Course in Statistical Inference by Wasserman (Quick crash course)
Probability and Statistics by DeGroot and Schervish
Mathematical Statistics with Applications by Wackerly, Mendenhall, and Scheaffer
>Combinatorics and Graph Theory
Combinatorics and Graph Theory by Harris, Hirst, and Mossinghoff
Combinatorics: Topics, Techniques, Algorithms by Cameron
An Introduction to the Analysis of Algorithms by Sedgewick and Flajolet
Analytic Combinatorics by Flajolet and Sedgewick (Sequel)
>Automata, Computability, and Complexity Theory
Automata and Computability by Kozen
Computational Complexity: A Modern Approach by Arora and Barak
Theory of Computation by Kozen
Introduction to Number Theory by Hardy and Wright
A Course in Computational Algebraic Number Theory by Cohen
Advanced Topics in Computational Number Theory by Cohen
Introduction to Modern Cryptography by Katz and Lindell
An Introduction to Mathematical Cryptography by Hoffstein, Pipher, and Silverman
Elements of Information Theory by Cover and Thomas
Coding and Information Theory by Roman
Algebra by Artin
Topics in Algebra by Herstein