Author : Dan Schneider
Publisher :
Page : 116 pages
File Size : 50,24 MB
Release : 2007
Category : Juvenile Fiction
ISBN : 9780439916486
The TeeNick block, featuring Zoey 101, is must-watch, must-TIVO, must-discuss viewing for tweens. Zoey Brooks and her friends Quinn and Lola are among the first girls to attend Pacific Coast Academy. The classes are tough, but getting the boys to accept them is even tougher! But there's lots of sunshine, good times and good friends to go around. And that's what it's all about, right? A brand new novel based on two episodes from the hit TV show.
Author : Rebecca L. Thomas
Publisher : Libraries Unlimited
Page : 1022 pages
File Size : 38,62 MB
Release : 2009
Category : Language Arts & Disciplines
ISBN :
Indexes popular fiction series for K-6 readers with groupings based on thematics, consistant setting, or consistant characters. Annotated entries are arranged alphabetically by series name and include author, publisher, date, grade level, genre, and a list of individual titles in the series. Volume is indexed by author, title, and subject/genre and includes appendixes suggesting books for boys, girls, and reluctant/ESL readers.
Author : Joe Zhu
Publisher : Springer Science & Business Media
Page : 334 pages
File Size : 44,94 MB
Release : 2007-06-08
Category : Business & Economics
ISBN : 0387716076
In a relatively short period of time, data envelopment analysis (DEA) has grown into a powerful analytical tool for measuring and evaluating performance. DEA is computational at its core and this book is one of several Springer aim to publish on the subject. This work deals with the micro aspects of handling and modeling data issues in DEA problems. It is a handbook treatment dealing with specific data problems, including imprecise data and undesirable outputs.
Author : De-Shuang Huang
Publisher : Springer Science & Business Media
Page : 707 pages
File Size : 34,65 MB
Release : 2010-07-30
Category : Computers
ISBN : 3642149219
This book constitutes the refereed proceedings of the 6th International Conference on Intelligent Computing, ICIC 2010, held in Changsha, China, in August 2010. The 85 revised full papers presented were carefully reviewed and selected from a numerous submissions. The papers are organized in topical sections on neural networks, evolutionary learning & genetic algorithms, fuzzy theory and models, fuzzy systems and soft computing, particle swarm optimization and niche technology, supervised & semi-supervised learning, unsupervised & reinforcement learning, combinatorial & numerical optimization, systems biology and computational biology, neural computing and optimization, nature inspired computing and optimization, knowledge discovery and data mining, artificial life and artificial immune systems, intelligent computing in image processing, special session on new hand based biometric methods, special session on recent advances in image segmentation, special session on theories and applications in advanced intelligent computing, special session on search based software engineering, special session on bio-inspired computing and applications, special session on advance in dimensionality reduction methods and its applications, special session on protein and gene bioinformatics: methods and applications.
Author : Parinya Sanguansat
Publisher : BoD – Books on Demand
Page : 304 pages
File Size : 42,75 MB
Release : 2012-03-02
Category : Computers
ISBN : 9535101951
This book is aimed at raising awareness of researchers, scientists and engineers on the benefits of Principal Component Analysis (PCA) in data analysis. In this book, the reader will find the applications of PCA in fields such as image processing, biometric, face recognition and speech processing. It also includes the core concepts and the state-of-the-art methods in data analysis and feature extraction.
Author : Field Cady
Publisher : John Wiley & Sons
Page : 420 pages
File Size : 34,88 MB
Release : 2017-02-28
Category : Mathematics
ISBN : 1119092949
A comprehensive overview of data science covering the analytics, programming, and business skills necessary to master the discipline Finding a good data scientist has been likened to hunting for a unicorn: the required combination of technical skills is simply very hard to find in one person. In addition, good data science is not just rote application of trainable skill sets; it requires the ability to think flexibly about all these areas and understand the connections between them. This book provides a crash course in data science, combining all the necessary skills into a unified discipline. Unlike many analytics books, computer science and software engineering are given extensive coverage since they play such a central role in the daily work of a data scientist. The author also describes classic machine learning algorithms, from their mathematical foundations to real-world applications. Visualization tools are reviewed, and their central importance in data science is highlighted. Classical statistics is addressed to help readers think critically about the interpretation of data and its common pitfalls. The clear communication of technical results, which is perhaps the most undertrained of data science skills, is given its own chapter, and all topics are explained in the context of solving real-world data problems. The book also features: • Extensive sample code and tutorials using Python™ along with its technical libraries • Core technologies of “Big Data,” including their strengths and limitations and how they can be used to solve real-world problems • Coverage of the practical realities of the tools, keeping theory to a minimum; however, when theory is presented, it is done in an intuitive way to encourage critical thinking and creativity • A wide variety of case studies from industry • Practical advice on the realities of being a data scientist today, including the overall workflow, where time is spent, the types of datasets worked on, and the skill sets needed The Data Science Handbook is an ideal resource for data analysis methodology and big data software tools. The book is appropriate for people who want to practice data science, but lack the required skill sets. This includes software professionals who need to better understand analytics and statisticians who need to understand software. Modern data science is a unified discipline, and it is presented as such. This book is also an appropriate reference for researchers and entry-level graduate students who need to learn real-world analytics and expand their skill set. FIELD CADY is the data scientist at the Allen Institute for Artificial Intelligence, where he develops tools that use machine learning to mine scientific literature. He has also worked at Google and several Big Data startups. He has a BS in physics and math from Stanford University, and an MS in computer science from Carnegie Mellon.
Author : Shai Shalev-Shwartz
Publisher : Cambridge University Press
Page : 415 pages
File Size : 45,27 MB
Release : 2014-05-19
Category : Computers
ISBN : 1107057132
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Author : Ignacio Ruiz
Publisher : John Wiley & Sons
Page : 471 pages
File Size : 36,83 MB
Release : 2021-12-28
Category : Business & Economics
ISBN : 1119791383
State-of-the-art algorithmic deep learning and tensoring techniques for financial institutions The computational demand of risk calculations in financial institutions has ballooned and shows no sign of stopping. It is no longer viable to simply add more computing power to deal with this increased demand. The solution? Algorithmic solutions based on deep learning and Chebyshev tensors represent a practical way to reduce costs while simultaneously increasing risk calculation capabilities. Machine Learning for Risk Calculations: A Practitioner’s View provides an in-depth review of a number of algorithmic solutions and demonstrates how they can be used to overcome the massive computational burden of risk calculations in financial institutions. This book will get you started by reviewing fundamental techniques, including deep learning and Chebyshev tensors. You’ll then discover algorithmic tools that, in combination with the fundamentals, deliver actual solutions to the real problems financial institutions encounter on a regular basis. Numerical tests and examples demonstrate how these solutions can be applied to practical problems, including XVA and Counterparty Credit Risk, IMM capital, PFE, VaR, FRTB, Dynamic Initial Margin, pricing function calibration, volatility surface parametrisation, portfolio optimisation and others. Finally, you’ll uncover the benefits these techniques provide, the practicalities of implementing them, and the software which can be used. Review the fundamentals of deep learning and Chebyshev tensors Discover pioneering algorithmic techniques that can create new opportunities in complex risk calculation Learn how to apply the solutions to a wide range of real-life risk calculations. Download sample code used in the book, so you can follow along and experiment with your own calculations Realize improved risk management whilst overcoming the burden of limited computational power Quants, IT professionals, and financial risk managers will benefit from this practitioner-oriented approach to state-of-the-art risk calculation.
Author : Field Cady
Publisher : John Wiley & Sons
Page : 208 pages
File Size : 50,59 MB
Release : 2020-12-30
Category : Business & Economics
ISBN : 1119544084
Tap into the power of data science with this comprehensive resource for non-technical professionals Data Science: The Executive Summary – A Technical Book for Non-Technical Professionals is a comprehensive resource for people in non-engineer roles who want to fully understand data science and analytics concepts. Accomplished data scientist and author Field Cady describes both the “business side” of data science, including what problems it solves and how it fits into an organization, and the technical side, including analytical techniques and key technologies. Data Science: The Executive Summary covers topics like: Assessing whether your organization needs data scientists, and what to look for when hiring them When Big Data is the best approach to use for a project, and when it actually ties analysts’ hands Cutting edge Artificial Intelligence, as well as classical approaches that work better for many problems How many techniques rely on dubious mathematical idealizations, and when you can work around them Perfect for executives who make critical decisions based on data science and analytics, as well as mangers who hire and assess the work of data scientists, Data Science: The Executive Summary also belongs on the bookshelves of salespeople and marketers who need to explain what a data analytics product does. Finally, data scientists themselves will improve their technical work with insights into the goals and constraints of the business situation.
Author : Rafael A. Irizarry
Publisher : CRC Press
Page : 836 pages
File Size : 46,58 MB
Release : 2019-11-20
Category : Mathematics
ISBN : 1000708039
Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert.
