Larousse gastronomique

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1. de_niedJan 2021
   As another post has stated, this isn't a very helpful post on learning to cook, and is more Amazon affiliate links than anything else. If people are genuinely interested in cooking and Culinary Arts, below is a book suggestion list, which should have free online PDF versions of them. This list is meant for people who wish to cook professionally, but for themselves, at home, for friends, so on. The list circles around recipes, theory, history, technique, so forth. More generally put, "kitchen knowledge."

   Note, these books are meant for dedicated inquisitive cooks who are eager to learn. If they're gonna sit on a coffee table or be used for average homemade meals, then these aren't for you. These books are the starting point and foundation of some of the most important chefs in history. It's up to you to Google the book and figure out what it's about and why it's so important (most should have Wiki pages). This is list is only meant to serve as a starting point for what to Google in the first place.

   All the books (should) have English translations, however they vary. Some translations leave things out, or give literal translations rather than the meaning, etc. The original title is used so you can have a source point to choose your own translations based on reviews/descriptions.

   Listed by order of importance (per my opinion). Mostly focused on haute cuisine and Italian, but another list for Japanese at the bottom. Note, I am biased against nouvelle cuisine, so you'll have to Google that for yourselves.

   [Haute Cuisine] Le Guide Culinaire - Auguste Escoffier

   [American/Haute] The Professional Chef - The Culinary Institute of America (As well as the Bakery/Pastry version)

   [Haute Cuisine] Le Répertoire de la Cuisine - Louis Saulnier

   [French/Haute Cuisine] Larousse Gastronomique - Prosper Montagné

   [Haute Cuisine] L'Art de la cuisine française au dix-neuvième siècle - Marie-Antoine Carême (Completed only 3/5 volumes before death)

   [Italian] La scienza in cucina e l'arte di mangiar bene - Pellegrino Artusi

   [Italian] Il cucchiaio d'argento - Domus

   [Haute Cuisine] Ma Gastronomie - Fernand Point

   [Italian] Ecco il tuo libro di cucina - Pellegrino Artusi (Still looking for a copy, much less a translated one)

   [Technique] La Technique [OR/AND] New Complete Techniques - Jacques Pepin

   [Why ___?] On Food and Cooking: The Science and Lore of the Kitchen - Harlod McGee

   [Pairing] The Flavor Bible - Andrew Dornenburg

   ---[Japanese]---:

   Japanese Cooking: A Simple Art

   Sushi: Jiro Gastronomy

   *[Next 3 are "maybes", haven't had time to read/research them fully.]

   Ivan Orkin (Chef focused on Ramen)

   Cooking with Kimiko-san

   Kimiko's World: Cooking, culture, and festivals of Japan

   ---[Additional]---:

   Ruhlman Twenty - Michael Ruhlman

   White Heat - Marco Pierre White

   Sauces - James Peterson

   Cookwise/Bakewise

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2. graycatJul 2017
   On the limitations of machine learning as in the OP, the OP is correct.

   So, right, current approaches to "machine learning* as in the OP have some serious "limitations". But this point is a small, tiny special case of something else much larger and more important: Current approaches to "machine learning" as in the OP are essentially some applied math, and applied math is commonly much more powerful than machine learning as in the OP and has much less severe limitations.

   Really, "machine learning" as in the OP is not learning in any significantly meaningful sense at all. Really, apparently, the whole field of "machine learning" is heavily just hype from the deceptive label "machine learning". That hype is deceptive, apparently deliberately so, and unprofessional.

   Broadly machine learning as in the OP is a case of old empirical curve fitting where there is a long history with a lot of approaches quite different from what is in the OP. Some of the approaches are under some circumstances much more powerful than what is in the OP.

   The attention to machine learning is omitting a huge body of highly polished knowledge usually much more powerful. In a cooking analogy, you are being sold a state fair corn dog, which can be good, instead of everything in Escoffier,

   Prosper Montagné, Larousse Gastronomique: The Encyclopedia of Food, Wine, and Cookery, ISBN 0-517-503336, Crown Publishers, New York, 1961.

   Essentially, for machine learning as in the OP, if (A) have a LOT of training data, (B) a lot of testing data, (C) by gradient descent or whatever build a model of some kind that fits the training data, and (D) the model also predicts well on the testing data, then (E) may have found something of value.

   But the test in (D) is about the only assurance of any value. And the value in (D) needs an assumption: Applications of the model will in some suitable sense, rarely made clear, be close to the training data.

   Such fitting goes back at least to

   Leo Breiman, Jerome H. Friedman, Richard A. Olshen, Charles J. Stone, Classification and Regression Trees, ISBN 0-534-98054-6, Wadsworth & Brooks/Cole, Pacific Grove, California, 1984.

   not nearly new. This work is commonly called CART, and there has long been corresponding software.

   And CART goes back to versions of regression analysis that go back maybe 100 years.

   So, sure, in regression analysis, we are given points on an X-Y coordinate system and want to fit a straight line so that as a function of points on the X axis the line does well approximating the points on the X-Y plot. Being more specific could use some mathematical notation awkward for simple typing and, really, likely not needed here.

   Well, to generalize, the X axis can have several dimensions, that is, accommodate several variables. The result is multiple linear regression.

   For more, there is a lot with a lot of guarantees. Can find those in short and easy form in

   Alexander M. Mood, Franklin A. Graybill, and Duane C. Boas, Introduction to the Theory of Statistics, Third Edition, McGraw-Hill, New York, 1974.

   with more detail but still easy form in

   N. R. Draper and H. Smith, Applied Regression Analysis, John Wiley and Sons, New York, 1968.

   with much more detail and carefully done in

   C. Radhakrishna Rao, Linear Statistical Inference and Its Applications: Second Edition, ISBN 0-471-70823-2, John Wiley and Sons, New York, 1967.

   Right, this stuff is not nearly new.

   So, with some assumptions, get lots of guarantees on the accuracy of the fitted model.

   This is all old stuff.

   The work in machine learning has added some details to the old issue of over fitting, but, really, the math in old regression takes that into consideration -- a case of over fitting will usually show up in larger estimates for errors.

   There is also spline fitting, fitting from Fourier analysis, autoregressive integrated moving average processes,

   David R. Brillinger, Time Series Analysis: Data Analysis and Theory, Expanded Edition, ISBN 0-8162-1150-7, Holden-Day, San Francisco, 1981.

   and much more.

   But, let's see some examples of applied math that totally knocks the socks off model fitting:

   (1) Early in civilization, people noticed the stars and the ones that moved in complicated paths, the planets. Well Ptolemy built some empirical models based on epi-cycles that seemed to fit the data well and have good predictive value.

   But much better work was from Kepler who discovered that, really, if assume that the sun stays still and the earth moves around the sun, then the paths of planets are just ellipses.

   Next Newton invented the second law of motion, the law of gravity, and calculus and used them to explain the ellipses.

   So, what Kepler and Newton did was far ahead of what Ptolemy did.

   Or, all Ptolemy did was just some empirical fitting, and Kepler and Newton explained what was really going on and, in particular, came up with much better predictive models.

   Empirical fitting lost out badly.

   Note that once Kepler assumed that the sun stands still and the earth moves around the sun, actually he didn't need much data to determine the ellipses. And Newton needed nearly no data at all except to check is results.

   Or, Kepler and Newton had some good ideas, and Ptolemy had only empirical fitting.

   (2) The history of physical science is just awash in models derived from scientific principles that are, then, verified by fits to data.

   E.g., some first principles derivations shows what the acoustic power spectrum of the 3 K background radiation should be, and the fit to the actual data from WMAP, etc. was astoundingly close.

   News Flash: Commonly some real science or even just real engineering principles totally knocks the socks off empirical fitting, for much less data.

   (3) E.g., here is a fun example I worked up while in a part time job in grad school: I got some useful predictions for an enormously complicated situation out of a little applied math and nearly no data at all.

   I was asked to predict what the survivability of the US SSBN fleet would be under a special scenario of global nuclear war limited to sea.

   Well, there was a WWII analysis by B. Koopman that showed that in search, say, of a submarine for a surface ship, an airplane for a submarine, etc. the encounter rates were approximately a Poisson process.

   So, for all the forces in that war at sea, for the number of forces surviving, with some simplifying assumptions, we have a continuous time, discrete state space Markov process subordinated to a Poisson process. The details of the Markov process are from a little data about detection radii and the probabilities at a detection, one dies, the other dies, both die, or neither die.

   That's all there was to the set up of the problem, the model.

   Then to evaluate the model, just use Monte Carlo to run off, say, 500 sample paths, average those, appeal to the strong law of large numbers, and presto, bingo, done. Also can easily put up some confidence intervals.

   The customers were happy.

   Try to do that analysis with big data and machine learning and will be in deep, bubbling, smelly, reeking, flaming, black and orange, toxic sticky stuff.

   So, a little applied math, some first principles of physical science, or some solid engineering data commonly totally knocks the socks off machine learning as in the OP.

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