Sunday, March 6, 2011

Spring 2011 Reading List

I gave last Spring's reading list (which was more what I had read in the previous year) in this post. This spring, I am going to write what I am currently reading or hope to finish this Spring.
  • Seven Languages in Seven Weeks - this book looks interesting, and certainly the challenge of trying to meaningfully cover seven languages and language philosophies in a short book will be interesting if nothing else.
  • Programming in Scala - I have "played" with Scala, but not done more than toy programs. However, from everything I know about it- I believe I will really enjoy it. I buy into a lot of the functional vs object oriented dualism, and certainly enjoy the terse syntax. I think there are still interesting questions with regards to software development economics (i.e. how do we integrate Scala in a team of mixed skill levels, cost trade offs, etc.)
  • Thinking Forth - I'm not particularly interested in Forth, but I am interested in language design and problem solving philosophies.
  • Domain Driven Design - this was recommended by a commenter in the previous post, and I picked it up. I've made it through quite a bit of the book, and have enjoyed it so far. I had always heard this book referenced by Fowler et al, and really I should've read it years ago...
  • Introduction to Reliable Distributed Programming - its a Springer book, 'nuff said. I've been through a few distributed computing books, and have a fairly broad knowledge of the space. I am hoping to get a more mature, theoretically rigorous view of the problems now.
  • The Art of Multiprocessor Programming - this book is great. I enjoyed the nice mix of intuitive description and theoretical rigor. It's a nice blend of theory and pragmatics. I really recommend reading this for anyone that wants in depth knowledge of parallel computing.
  • Introductions to Neural Networks for Java - this was an interesting book to skim I don't really want to recommend it, because most of it is explaining his neural network code base instead of the underlying concepts. In any case, its a nice thing to skim over an afternoon.
  • Neural Networks and Learning Machines - Great textbook for all the theoretical background and mathematical proofs regarding Neural Nets and machine learning. I've had to crack open my calc books to freshen up while going through this...its dense.
  • Fuzzy Models and Genetic Algorithms for Data Mining and Exploration - I can't recommend this book (see my Amazon review if you're curious why), but it does provide a decent vocab overview for the topics it covers.

Well that's it for the moment! This spring is a bit more theory heavy than the previous list... that probably reflects my work and school change, which has been more research oriented in the last year. I'm not moving away from the real world just trying to get a more comprehensive grasp on both worlds in the areas which I am interested. I've always thought this was one of my strengths-- knowing enough theory to shape my thinking skills and be knowledgeable-- but continuously, deliberately enriching my implementation and practical skills to actually put the theory to good use! The intersection of the two is the more rewarding area for me, I think.

Steve

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