Neural networks are a key element of deep learning and artificial intelligence, which today is capable of some truly impressive feats. Yet too few really understand how neural networks actually work. This guide will take you on a fun and unhurried journey, starting from very simple ideas, and gradually building up an understanding of how neural networks work. You won't need any mathematics beyond secondary school, and an accessible introduction to calculus is also included. The ambition of this guide is to make neural networks as accessible as possible to as many readers as possible - there are enough texts for advanced readers already! You'll learn to code in Python and make your own neural network, teaching it to recognise human handwritten numbers, and performing as well as professionally developed networks. Part 1 is about ideas. We introduce the mathematical ideas underlying the neural networks, gently with lots of illustrations and examples. Part 2 is practical. We introduce the popular and easy to learn Python programming language, and gradually builds up a neural network which can learn to recognise human handwritten numbers, easily getting it to perform as well as networks made by professionals. Part 3 extends these ideas further. We push the performance of our neural network to an industry leading 98% using only simple ideas and code, test the network on your own handwriting, take a privileged peek inside the mysterious mind of a neural network, and even get it all working on a Raspberry Pi. All the code in this has been tested to work on a Raspberry Pi Zero.

    Building on the field of mathematical statistics, probabilistic robotics endows robots with a new level of robustness in real-world situations. This book introduces the reader to a wealth of techniques and algorithms in the field. All algorithms are based on a single overarching mathematical foundation. Each chapter provides example implementations in pseudo code, detailed mathematical derivations, discussions from a practitioner's perspective, and extensive lists of exercises and class projects. The book's Web site, www.probabilistic-robotics.org, has additional material. The book is relevant for anyone involved in robotic software development and scientific research. It will also be of interest to applied statisticians and engineers dealing with real-world sensor data.

    After introducing the basic concepts, the book examines techniques for modeling probability density functions and the properties and merits of the multi-layerperceptron and radial basis function network models. Also covered are various forms of error functions, principal algorithms for error function minimalization, learning and generalization in neural networks, and Bayesian techniques and their applications. Designed as a text, with over 100exercises, this fully up-to-date work will benefit anyone involved in the fields of neural computation and pattern recognition.

    Mitchell covers the field of machine learning, the study of algorithms that allow computer programs to automatically improve through experience and that automatically infer general laws from specific data.

    Included are step-by-step instructions on how to carry out Bayesian data analyses.Download Link : readbux.com/download?i=0124058884            0124058884 Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan PDF by John Kruschke

    

    

    Now with the second edition, readers will find information on key new topics such as neural networks and statistical pattern recognition, the theory of machine learning, and the theory of invariances. Also included are worked examples, comparisons between different methods, extensive graphics, expanded exercises and computer project topics.An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.

    The book makes clear that applied microeconometrics is about the estimation of marginal and treatment effects, and that parametric estimation is simply a means to this end. It also clarifies the distinction between causality and statistical association. The book focuses specifically on cross section and panel data methods. Population assumptions are stated separately from sampling assumptions, leading to simple statements as well as to important insights. The unified approach to linear and nonlinear models and to cross section and panel data enables straightforward coverage of more advanced methods. The numerous end-of-chapter problems are an important component of the book. Some problems contain important points not fully described in the text, and others cover new ideas that can be analyzed using tools presented in the current and previous chapters. Several problems require the use of the data sets located at the author's website.

    The authors provide precise definitions and full proofs of results, sacrificing generalities and limiting the scope of the material in order to do so. The text is organized in four parts: strategic games, extensive games with perfect information, extensive games with imperfect information, and coalitional games. It includes over 100 exercises. Solution ManualTable of Contents, Errata, and more...

    Organized around a strategic marketing framework"Services Marketing" guides readers into the consumer and competitive environments in services marketing. The marketing framework has been restructured for this edition to reflect what is happening in services marketing today.

    To accomplish this, OBM, 8E achieves the difficult goal of preserving its key strengths (i.e., thorough, current, good balance of research and practice) while streamlining its content by removing over 100 pages of readings that are now available on the Web. This reduction in page length makes the book more affordable, teachable, and efficient for students. Preserving Scholarship While Streamlining captures the spirit of what I/K/M used as the guiding principle while writing OBM 8E.

    For anyone who has ever wondered how computers solve problems, an engagingly written guide for nonexperts to the basics of computer algorithms.

    Froeb/McCann's MANAGERIAL ECONOMICS: A PROBLEM SOLVING APPROACH, 2E covers traditional material using a problem-based pedagogy built around common business mistakes. Models are used sparingly, and then only to the extent that they help students figure out why mistakes are made, and how to fix them. This edition's succinct, fast-paced presentation and challenging, interactive applications place students in the role of a decision maker who has to identify mistakes that reduce profits, and propose solutions to bring profits back up. The lively book provides an excellent ongoing reference for students pursuing business careers. New chapters and updates highlight mistakes that precipitated the financial crisis. With MANAGERIAL ECONOMICS, 2E your students are taught to use economics to not only identify profitable decisions, but also how to implement them within an organization.

    Johnson. It was the first book exclusively on the theory of NP-completeness and computational intractability. The book features an appendix providing a thorough compendium of NP-complete problems (which was updated in later printings of the book). The book is now outdated in some respects as it does not cover more recent development such as the PCP theorem. It is nevertheless still in print and is regarded as a classic: in a 2006 study, the CiteSeer search engine listed the book as the most cited reference in computer science literature.