How we did it:
For any feedback, any questions, any notes or just for chat - feel free to follow us on social networks
Illustrating some of the most common misconceptions and pitfalls software developers face using relational databases, this book helps readers use a database to produce the most efficient results, and turn sluggish, inflexible code into high-quality, reliable solutions.
Christopher M. Bishop
The field of pattern recognition has undergone substantial development over the years. This book reflects these developments while providing a grounding in the basic concepts of pattern recognition and machine learning. It is aimed at advanced undergraduates or first year PhD students, as well as researchers and practitioners.
In Adrenaline Junkies and Template Zombies, the six principal consultants of The Atlantic Systems Guild present the patterns of behavior they most often observe at the dozens of IT firms they transform each year, around the world. The result is a quick-read guide to identifying nearly ninety typical scenarios, drawing on a combined one-hundred-and-fifty years of project management experience. Project by project, you'll improve the accuracy of your hunches and your ability to act on them. The patterns are presented in an easy-reference format, with names designed to ease communication with your teammates. In just a few words, you can describe what's happening on your project. Citing the patterns of behavior can help you quickly move those above and below you to the next step on your project. Not every pattern will be evident in your organization, and not every pattern is necessarily good or bad. However, you'll find many patterns that will apply to your current and future assignments, even in the most ambiguous circumstances. When you assess your situation and follow your next hunch, you'll have the collective wisdom of six world-class consultants at your side.
Michael Mitzenmacher, Eli Upfal
Randomization and probabilistic techniques play an important role in modern computer science, with applications ranging from combinatorial optimization and machine learning to communication networks and secure protocols.Assuming only an elementary background in discrete mathematics, this textbook is designed to accompany a one- or two-semester course for advanced undergraduates or beginning graduate students in computer science and applied mathematics. It gives an excellent introduction to the probabilistic techniques and paradigms used in the development of probabilistic algorithms and analyses, including random sampling, expectations, Markov's and Chevyshev's inequalities, Chernoff bounds, balls and bins models, the probabilistic method, Markov chains, MCMC, martingales, entropy, and other topics.