Classic Computer Science Problems in Python

This book is great for semi-experienced python users. Every chapter introduces several new pythonic concepts and provides a very nice generic framework for trying out the algorithms described. It is the kind of book where you'd get the most out of it when you work through it.

I am a season programer and really enjoyed this book. Found examples to be quite complete. It has helped me improve my Python skills.

I found the topics in this book to be fascinating which is why I picked it up and was hopeful that a self taught developer would be able to find some use for this book. To which I have found some nuggets of helpful tips. It broke down bit shifting in a pretty digestible manner.However with all the above said I found the book very disorganized. For example in chapter one the purpose was on the discussion about small problems. We go straight into the fibonacci sequence (which did give some very helpful hints on recursion) but then right after talking about that we go straight into memoization without any segue. Then after that we go right into trivial compression then after 4 to 4 1/2 pages we go right into unbreakable encryption. With each change in topic I was left with trying to figure out what the author wanted me to gain and how did those specifically classify as small problems.I gave it a 3 because this book does have some very cool insights and taking the examples and making them my own has helped but for someone without a CS degree I was left wanting more depth and that is something that I didn't get.

Great book.The material is very well explained.

I particularly like the way Professor Kopec organized the topics, code and explanations. Although the book lacks complexity analysis, it is an excellent book for someone who is picking up Python and reviewing CS topics at the same time. The topics selected by Professor Kopec aren't the same as those you would find in a (rather boring, I should say) data structure and algorithm book where C/Java code snippets are (sometimes in brute force) translated to Python code snippets. The topics in this book build up a repertoire of re-usable frameworks for the "basics" such as search, CSP, and graph algorithms as an illustration of how generic framework can be constructed in Python to solve classes of similar problems. In later half of the book Professor Kopec leads the reader into the AI realm of things (at the introductory level) such as neural networks and game theory. Very enjoyable read and great learning. (Disclaimer: I purchased the book at the publisher's site at 50% bundle-discount.)

I absolutely love Classic Computer Science Problems in Python. It taught me both how to use Python in ways I never had before and about some computer science concepts that I may have heard of but had never used for actual coding projects. The book presents a large amount of information in a surprisingly small number of pages. I appreciate that the description of the material is to-the-point and not mathematical. This is the only computer science book I’ve read cover-to-cover in probably ten years. It was extremely useful in working through the Advent of Code problem sets and, directly because of this book, I’ve used A*, Dijkstra’s algorithm, and genetic algorithms in projects at work. I liked Classic Computer Science Problems in Python so much that I also bought Classic Computer Science Problems in Swift to help me improve my Swift skills.

I’ve played around considerably with neural networks (as a student) and used TensorFlow to implement them, but it was really cool to see one implemented entirely in Python. It allowed me to play around and inspect the neurons and weights, etc. That was a really helpful set of exercises to not just “learn” about them, but to actually watch one in action.This book is supported by a community with active participation from the author, so you really get a lot more than just a book. Highly reccomended.

This book is ok. It would have been better if it were not for the author's decision to use type hints in all the examples. Typing hints are relatively new, and if you just learned python and are looking to get a little more classic computer science knowledge, they only get in the way. Not that type hints are hard to understand, it just clutters up the code if you are not concerned about production, only illustrating concepts the reader is trying to learn.

If you've already done a decent amount of programming in at least two other languages (including at least one OO), this is a great book to learn about python from. It does not bother with the simple things that you can look up online in two seconds (flow control syntax, defining a simple function, etc.) or OOP concepts. Instead it is a very well chosen set of, as the title implies, classic algorithms with broad use value: K-means clustering, graph searching, constraint-satisfaction problems, even neural network basics. Each of these forms a chapter with a simple, comprehensible generic implementation, presented piecemeal in a narrative and then applied to a handful of different concrete problems. I'd seen most of these techniques before, but I wish my original introduction to them had been this succinct and balanced.Along the way of various features and conventions of python are introduced in a natural way. The author also uses the relatively new (not strictly enforced) typing annotations, which I appreciated as a fan of strong typing. Again, though, if you are out to learn programming with python, this probably is not the book for you. But if you already understand OOP well and want something interesting to survey a new language with, this is a lot of fun.

I've been doing CS / programming for 20 years now, and a professional for 13 years already. Yet I just got this book and for the small size of it, it's just awesome how good quality it is. After 20 years and still can learn new approaches to solving classical problems, new ways to express algorithms, make them simpler, more readable, or better explained etc. I've coded breadth-first tree traversals countless of times, but it's so well structured in this book that I can now visualise it easier. It also goes as far as machine learning.You don't have to be a Python guru for it. If you know Java or any other language, then a couple of days of getting acquainted with Python will be enough to understand this book well.

It directly and straight starts with the examples and problems, and it avoids to mention too much the good old time of the 60s, 70s, ... This format helps a lot to keep the stuff clean and highlight the quintessential. For me, the book is a must-have, because it embraces the basic and essential standard algorithms such as searching, sorting, Genetic, neural network, and more ... and therefore it just a good friend

These classic algorithms are useful to know but the way the book is presented is not very friendly - I bought to help my A level students but no so much help here

I've only read the first couple chapters. The explanations are clear thus far and it's easy to follow along for someone with coding experience but no traditional CS background.