 Home | Amazing | Today | Tags | Publishers | Years | Account | Search       Python for Data Science For Dummies (For Dummies (Computer/Tech)) The fast and easy way to learn Python programming and statistics Python is a general-purpose programming language created in the late 1980s?and named after Monty Python?that's used by thousands of people to do things from testing microchips at Intel, to powering Instagram, to building video games with the...   Introduction to Probability Models Introduction to Probability Models, Twelfth Edition, is the latest version of Sheldon Ross's classic bestseller. This trusted book introduces the reader to elementary probability modelling and stochastic processes and shows how probability theory can be applied in fields such as engineering, computer science, management...   Stochastic Flows and Jump-Diffusions (Probability Theory and Stochastic Modelling, 92) This monograph presents a modern treatment of (1) stochastic differential equations and (2) diffusion and jump-diffusion processes. The simultaneous treatment of diffusion processes and jump processes in this book is unique: Each chapter starts from continuous processes and then proceeds to processes with jumps. In...  Digital Dice: Computational Solutions to Practical Probability Problems"Paul Nahin's Digital Dice is a marvelous book, one that is even better than his Duelling Idiots. Nahin presents twenty-one great probability problems, from George Gamow's famous elevator paradox (as corrected by Donald Knuth) to a bewildering puzzle involving two rolls of toilet paper, and he solves them all with the aid...   Probability and Statistics for Computer Scientists Student-Friendly Coverage of Probability, Statistical Methods, Simulation, and Modeling Tools Incorporating feedback from instructors and researchers who used the previous edition, Probability and Statistics for Computer Scientists, Second Edition helps students understand general methods of stochastic...   Mathematical Methods for Physics and Engineering: A Comprehensive GuideFrom reviews of previous editions: '...a great scientific textbook. It is a tour de force ... to write mathematical sections that are both complete and at an appropriate academic level. The authors have clearly succeeded in this challenge, making this a remarkable pedagogical book ... The choice of exercises is excellent and possibly the best...  Bayesian Statistics the Fun Way: Understanding Statistics and Probability with Star Wars, LEGO, and Rubber Ducks Fun guide to learning Bayesian statistics and probability through unusual and illustrative examples. Probability and statistics are increasingly important in a huge range of professions. But many people use data in ways they don't even understand, meaning they aren't getting the most from it. Bayesian...   Bayesian Data Analysis, Second Edition (Chapman & Hall/CRC Texts in Statistical Science) This book is intended to have three roles and to serve three associated audiences: an introductory text on Bayesian inference star ting from first principles, a graduate text on effective current approaches to Bayesian modeling and computation in statistics and related fields, and a handbook of Bayesian meth ods in applied...   Modern Statistical Methods for Astronomy: With R Applications Modern astronomical research is beset with a vast range of statistical challenges, ranging from reducing data from megadatasets to characterizing an amazing variety of variable celestial objects or testing astrophysical theory. Linking astronomy to the world of modern statistics, this volume is a unique resource, introducing astronomers to... Beginning Application Development with TensorFlow and Keras: Learn to design, develop, train, and deploy TensorFlow and Keras models as real-world applications

You need much more than imagination to predict earthquakes and detect brain cancer cells. Become an expert in designing and deploying TensorFlow and Keras models, and generate insightful predictions with the power of deep learning.

#### Key Features

• Cover the basics of neural networks and...  How to Think About Algorithms
There are many algorithm texts that provide lots of well-polished code and proofs of correctness. This book is not one of them. Instead, this book presents insights, notations, and analogies to help the novice describe and think about algorithms like an expert. By looking at both the big picture and easy step-by-step methods for developing...  Hidden Markov Models for Time Series: An Introduction Using R (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)

Reveals How HMMs Can Be Used as General-Purpose Time Series Models

Implements all methods in R
Hidden Markov Models for Time Series: An Introduction Using R applies hidden Markov models (HMMs) to a wide range of time series types, from continuous-valued, circular, and
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