Addison Wesley Science 10 PDF Download
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Author: Lionel Sandner
Publisher: Toronto: Pearson Education Canada
ISBN: 9780201729689
Category : Science
Languages : en
Pages : 510
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Book Description
Author: Lionel Sandner
Publisher: Toronto: Pearson Education Canada
ISBN: 9780201729689
Category : Science
Languages : en
Pages : 510
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Book Description
Author: Gordon J. Coleman
Publisher: Addison-Wesley Longman
ISBN:
Category : Mathematics
Languages : en
Pages : 292
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Book Description
Brings together a broad range of essential science information. Both fundamental and advanced concepts are presented in table, glossaries and summaries for quick memory refreshers at all levels.
Author: Susan B. Horwitz
Publisher: Addison Wesley Publishing Company
ISBN: 9780201357554
Category : Computers
Languages : en
Pages : 324
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Book Description
This complete test guide for AP Computer Science Exam in C++ features four comprehensive sample exams to prepare for the exam day, covers a full range of C++ topics, includes an extensive glossary that provides quick reference and contains test-taking hints that pinpoint the important aspects of the questions included on the exam.
Author: Lawrence H. Van Vlack
Publisher: Addison Wesley Publishing Company
ISBN:
Category : Technology & Engineering
Languages : en
Pages : 574
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Book Description
Author: Antony C. Wilbraham
Publisher:
ISBN:
Category : Chemistry
Languages : en
Pages :
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Book Description
Author: Jared P. Lander
Publisher: Addison-Wesley Professional
ISBN: 0134546997
Category : Computers
Languages : en
Pages : 1456
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Book Description
Statistical Computation for Programmers, Scientists, Quants, Excel Users, and Other Professionals Using the open source R language, you can build powerful statistical models to answer many of your most challenging questions. R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone, Second Edition, is the solution. Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modeling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you’ll need to accomplish 80 percent of modern data tasks. Lander’s self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. You’ll download and install R; navigate and use the R environment; master basic program control, data import, manipulation, and visualization; and walk through several essential tests. Then, building on this foundation, you’ll construct several complete models, both linear and nonlinear, and use some data mining techniques. After all this you’ll make your code reproducible with LaTeX, RMarkdown, and Shiny. By the time you’re done, you won’t just know how to write R programs, you’ll be ready to tackle the statistical problems you care about most. Coverage includes Explore R, RStudio, and R packages Use R for math: variable types, vectors, calling functions, and more Exploit data structures, including data.frames, matrices, and lists Read many different types of data Create attractive, intuitive statistical graphics Write user-defined functions Control program flow with if, ifelse, and complex checks Improve program efficiency with group manipulations Combine and reshape multiple datasets Manipulate strings using R’s facilities and regular expressions Create normal, binomial, and Poisson probability distributions Build linear, generalized linear, and nonlinear models Program basic statistics: mean, standard deviation, and t-tests Train machine learning models Assess the quality of models and variable selection Prevent overfitting and perform variable selection, using the Elastic Net and Bayesian methods Analyze univariate and multivariate time series data Group data via K-means and hierarchical clustering Prepare reports, slideshows, and web pages with knitr Display interactive data with RMarkdown and htmlwidgets Implement dashboards with Shiny Build reusable R packages with devtools and Rcpp Register your product at informit.com/register for convenient access to downloads, updates, and corrections as they become available.
Author: Andrew Kelleher
Publisher: Addison-Wesley Professional
ISBN: 0134116569
Category : Computers
Languages : en
Pages : 465
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Book Description
Foundational Hands-On Skills for Succeeding with Real Data Science Projects This pragmatic book introduces both machine learning and data science, bridging gaps between data scientist and engineer, and helping you bring these techniques into production. It helps ensure that your efforts actually solve your problem, and offers unique coverage of real-world optimization in production settings. –From the Foreword by Paul Dix, series editor Machine Learning in Production is a crash course in data science and machine learning for people who need to solve real-world problems in production environments. Written for technically competent “accidental data scientists” with more curiosity and ambition than formal training, this complete and rigorous introduction stresses practice, not theory. Building on agile principles, Andrew and Adam Kelleher show how to quickly deliver significant value in production, resisting overhyped tools and unnecessary complexity. Drawing on their extensive experience, they help you ask useful questions and then execute production projects from start to finish. The authors show just how much information you can glean with straightforward queries, aggregations, and visualizations, and they teach indispensable error analysis methods to avoid costly mistakes. They turn to workhorse machine learning techniques such as linear regression, classification, clustering, and Bayesian inference, helping you choose the right algorithm for each production problem. Their concluding section on hardware, infrastructure, and distributed systems offers unique and invaluable guidance on optimization in production environments. Andrew and Adam always focus on what matters in production: solving the problems that offer the highest return on investment, using the simplest, lowest-risk approaches that work. Leverage agile principles to maximize development efficiency in production projects Learn from practical Python code examples and visualizations that bring essential algorithmic concepts to life Start with simple heuristics and improve them as your data pipeline matures Avoid bad conclusions by implementing foundational error analysis techniques Communicate your results with basic data visualization techniques Master basic machine learning techniques, starting with linear regression and random forests Perform classification and clustering on both vector and graph data Learn the basics of graphical models and Bayesian inference Understand correlation and causation in machine learning models Explore overfitting, model capacity, and other advanced machine learning techniques Make informed architectural decisions about storage, data transfer, computation, and communication Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Author: Jeffrey D. Ullman
Publisher: Addison Wesley Publishing Company
ISBN:
Category : Computers
Languages : en
Pages : 344
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Book Description
Author: Sara Baase
Publisher: Pearson Education India
ISBN: 9788131702444
Category :
Languages : en
Pages : 710
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Book Description
Author: Eugene Charniak
Publisher: Addison Wesley Publishing Company
ISBN: 9780201119459
Category : Computers
Languages : en
Pages : 724
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Book Description
