Bayesian inference provides a simple and unified approach to data analysis, allowing experimenters to assign probabilities to competing hypotheses of interest, on the basis of the current state of knowledge. By incorporating relevant prior information, it can sometimes improve model parameter estimates by many orders of magnitude. This book...
State-of-the-art analysis of geological structures has become increasingly quantitative but traditionally, graphical methods are used in teaching. This innovative lab book provides a unified methodology for problem-solving in structural geology using linear algebra and computation. Assuming only limited mathematical training, the book begins...
For introductory courses in C Programming. Also for courses in Programming for Engineers, Programming for Business, and Programming for Technology.
The Deitels' How to Program series offers unparalleled breadth and depth of object-oriented programming concepts and intermediate-level topics for further study. Using the...
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Proven security tactics for today's mobile apps,devices, and networks
Volume I presents one-variable calculus with an introduction to linear algebra and volume II presents multi-variable calculus and linear algebra, with applications to differential equations and probability
One of the most important tasks in finance is to find good mathematical models for financial products, in particular derivatives. However, the more realistic the model, the more practitioners face still-unsolved problems in rigorous mathematics and econometrics, in addition to serious numerical difficulties. The idea behind this book is to...
Develop, validate, and deploy powerful decentralized applications using blockchain
Get the most out of cutting-edge blockchain technology using the hands-on information contained in this comprehensive resource. Written by a team of technology and legal experts, Blockchain:...
Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models. Each chapter guides you...
Can machine learning techniques solve our computer security problems and finally put an end to the cat-and-mouse game between attackers and defenders? Or is this hope merely hype? Now you can dive into the science and answer this question for yourself. With this practical guide, you’ll explore ways to apply machine learning to security...
Learn how to program in Python while making and breaking ciphers—algorithms used to create and send secret messages!
After a crash course in Python programming basics, you’ll learn to make, test, and hack programs that encrypt text with classical ciphers like the transposition cipher and Vigenère...
Learn how to solve challenging machine learning problems with TensorFlow, Google’s revolutionary new software library for deep learning. If you have some background in basic linear algebra and calculus, this practical book introduces machine-learning fundamentals by showing you how to design systems capable of detecting objects...
Understand, evaluate, and visualize data About This Book - Learn basic steps of data analysis and how to use Python and its packages - A step-by-step guide to predictive modeling including tips, tricks, and best practices - Effectively visualize a broad set of analyzed data and generate effective results Who This Book Is For This book is for...
