ML

This book treats the reader as a child. Anyone with any programming experience(including VB), would be better suited reading a book about LISP or Scheme.

The Little MLer takes the Socratic method to the extreme. The book is laid out in two columns, basically a question and an answer, and that's it. The authors make light of their subject matter, which can be entertaining, but the format definitely kills its utility as a reference. The one-page index is not very helpful. The book does take a dramatic turn in complexity around page 81, just short of the halfway point (in a book full of white space, mind you), when the truly useful fundamentals of functional programming are finally taken up, so that the reader with prior experience no longer feels coddled. It would make this book so much more useful if the authors included a reference section at the end, perhaps with the definitions of the SML '97 basis library, with which a professional can now do real-world application programming. This is a nice and at times fun introduction to ML that gives the reader a hint as to the true power and complexity of functional programming, but buyers should be aware THIS WILL NOT BE THE ONLY ML BOOK YOU WILL NEED.

This is an excellent little book that will subvert your programming style (for the better) if you read it at all closely. It teaches quite a bit about ML programming (and indeed functional programming in general) in small snippets.

Highly recommended for experienced programmers and novices alike.

This book has a lot of valuable information with regard to the construction of a compiler, that fact is overshadowed by the amazingly poor editting.

This is supposedly the "first edition reprint with corrections", but it seems like the vast majority of errors are yet to be corrected.

This book aims at teaching the reader how to write compilers and interpreters. Compared to other books (most notably the Dragon book - Aho, Sethi & Ullman: Compilers - Principles, Techniques and Tools) the structure and organization of the compiler he teaches are better and more in sync with modern compiler research. So although ML is not a language that is used much in practice it is well choosen for teaching compilers.

The first part of the book is intended for undergraduate Computer Science university student and the second part is for graduate students. I have only read the first part and browsed the second part. The first part is founded on the development of a compiler - the Tiger compiler - and that is the main flaw of the book. The Tiger compiler is fairly advanced and it is built up one phase at the time (see the chapter headings in the synopsis). A undergraduate reader who has never read anything about compiler design before will be very confused. The book is good in the way that it presents many good compiler design ideas and I learned some from it, but for me it was not the first book on compiler. I just think that the first part of the book has too steep a learning curve - even if you are proficient with ML, which is an absolute must. Recommended for serious readers who already know something about compiler construction before reading it. For other people I recommend the older but somewhat outdated book: Aho, Sethi & Ullman: Compilers - Principles, Techniques and Tools.

Appel explains the structure of a compiler in a very clear and precise fashion, including a lot of quite modern techniques. *However* it is only clear and precise if you have a rough high-level understanding of compilers and a good knowledge of ML beforehand. (If you understand one of the two, it is an excellent book for learning the other but you will need to do some thinking for yourself.)

If you're looking to learn about compilers from the very beginning, buy something else (I liked "Crafting A Compiler" as a starting point). If you're wanting to get a lot more precise about the way you build compilers, buy this book.