A long, detailed, and in depth look at every (?) facet of game balance; a one-stop-shop for all your game balance needs! The first half of the book talks about different ways a game can be/feel balance/unbalanced and how each different genre has its own balance requirements. The second half of the book offers up concrete advice on how to actually test and implement balance from a mostly mathematical perspective. At the end of the book there is a good overview of some of the more powerful spreadsheet capabilities that can assist in balancing your game. Overall, this is a great read for anyone wanting to know more about game balance (surprising I know) in general (the first half) and how to crank the numbers to make it happen (the second half).
Godel, Escher, Bach - An Eternal Golden Braid
The book builds in a very approachable manner, for even the most uninitiated reader, from basic formal systems to DNA and artificial intelligence. Each step is presented first with a humorous take in the form of a dialog between characters like Achilles and Tortoise and then the idea is elaborated on in a more formal manner with intellectual exercises that should engage the reader. Also the progression is made with a great many analogies where things like music, memory, DNA, and other seemingly unrelated topics are woven together to give a purchase point from many angles and thus be accessible to a great many people with a variety of backgrounds. I can say that I personally, with those 40 years of hindsight, don't give much credibility to some of the future (current) prospects of AI, but non-the-less find the book interesting and a worthwhile read for anyone interested in the topics. Overall the book is funny, informative, and a great introduction to some topics that 40+ years later we are still grappling with.
How to Solve It - A New Aspect of Mathematical Method
This book is much more than a simple, or complex, math book --it is be a must read for teachers in general. The most important lesson of the book, in my opinion, is that teachers (besides repeating, repeating, repeating) need not 'give it all away.' The student should never be robbed of working out a solution for themselves; slogging thought the difficulties, feeling the thrill of the hunt, and finally experiencing the joy of victory will instill much more than any rote lesson could ever achieve. Other than that broad advice, the book lays out, in mathematical terms (though it could be applied most generally), how to problem solve by planning around what you know and what you want to know; catalog your knows and unknowns, draw a picture, and work from the end towards the beginning (reverse planning). Other ideas concerning how you might tackle a problem are modifying the problem with the addition or subtraction of data to make it similar to a more tractable problem you are familiar with and pointing out that even failing is progress as long as you learn something in your failure.
Basics of Linear Algebra for Machine Learning - Discover the Mathematical Language of Data in Python
Slow to start but then picks up the pace as it moves along. gIt is not proof heavy; if you want a full blown linear algebra book this is not it. There are plenty of examples of how to implement various linear algebra operations in python/numpy that are extremely well explained. For someone familiar with the concepts, either python or linear algebra, it may seem a little remedial. All in all it was a little too simple for my tastes (but an enjoyable, breezy, casual read), but as an introduction used before moving on to more advanced books, or for someone that wants to dive right into machine learning who is OK with a less nitty-gritty and more practical approach, it is ideal.
Cryptography and Network Security Principles and Practice
After a brief introduction to historical cryptography, a quick and dirty crash course of the mathematics required to understand most of the book is presented. Various cypher types, or modes, are discussed, including block, stream, chain, feedback, and counter in addition to symmetric/asymmetric cyphers, public/private key encryption and exchange, particular attention is paid to the Diffie-Hellman method of exchange. Some of the cryptographic algorithms covered include DES, AES, and RSA; these are presented alongside various hashing algorithms and it is explained how the use of certain combinations of these tools can provide, integrity, confidentiality, authentication, or any combination of these to data. The cryptographic section concludes with a pair of chapters on trust/key management, and user authentication to close a decidedly well rounded and complete examination of the aforementioned topics. The network security portion of the book covers everything from transport layer security like HTTPS and SSH, wireless security, email security, and includes a final chapter on IP security.
Power Function Model
Notes and an example on how to use python to create and plot a power function model in python using numpy, scipy and matplotlib.