Check out these books below by Cambridge University Press.
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Small Summaries for Big Data
The massive volume of data generated in modern applications can
overwhelm our ability to conveniently transmit, store, and index it. For
many scenarios, building a compact summary of a dataset that is vastly
smaller enables flexibility and efficiency in a range of queries over
the data, in exchange for some approximation...
Summaries are described for specific types of data, such as geometric
data, graphs, and vectors and matrices. The authors offer detailed
descriptions of and pseudocode for key algorithms that have been
incorporated in systems from companies such as Google, Apple, Microsoft,
Netflix and Twitter.
- Examples, figures, and pseudocode enhance understanding of fundamentals and applications
- Written in accessible plain English
- Optional sections of advanced technical material provide further reading for experts without overwhelming novices
Date Published: November 2020
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Foundations of Probabilistic Programming
What does a probabilistic program actually compute? How can one formally reason about such probabilistic programs?...
Excel tables, program testing, and approximate computing. This title is also available as Open Access on Cambridge Core.
- Overview of theoretical underpinnings and applications of probabilistic programming
- Comprehensive survey chapters, accessible to graduate students and non-experts
- This title is also available as Open Access on Cambridge Core
Publication planned for: December 2020
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125 Problems in Text Algorithms with Solutions
String matching is one of the oldest algorithmic techniques, yet still
one of the most pervasive in computer science. The past 20 years have
seen technological leaps in applications as diverse as information
retrieval and compression...
The problems are drawn from a large range of scientific publications,
both classic and new. Building up from the basics, the book goes on to
showcase problems in combinatorics on words (including Fibonacci or
Thue-Morse words), pattern matching (including Knuth-Morris-Pratt and
Boyer-Moore like algorithms), efficient text data structures (including
suffix trees and suffix arrays), regularities in words (including
periods and runs) and text compression (including Huffman, Lempel-Ziv
and Burrows-Wheeler based methods).
Publication planned for: February 2021
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Mathematical Foundations of Infinite-Dimensional Statistical Models
In nonparametric and high-dimensional statistical models, the classical
Gauss–Fisher–Le Cam theory of the optimality of maximum likelihood
estimators and Bayesian posterior inference does not apply, and new
foundations and ideas have been developed in the past several decades...
This includes the basic theory of convolution kernel and projection
estimation, but also Bayesian nonparametrics and nonparametric maximum
likelihood estimation. In a final chapter the theory of adaptive
inference in nonparametric models is developed, including Lepski's
method, wavelet thresholding, and adaptive inference for self-similar
functions. Winner of the 2017 PROSE Award for Mathematics...
Publication planned for: February 2021
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Uncertainty Analysis for Engineers and Scientists A Practical Guide
Build the skills for determining appropriate error limits for quantities
that matter with this essential toolkit. Understand how to handle a
complete project and how uncertainty enters into various steps...
Whether you are new to the sciences or an experienced engineer, this
useful resource provides a practical approach to performing error
analysis. Suitable as a text for a junior or senior level laboratory
course in aerospace, chemical and mechanical engineering, and for
professionals.
- Organizes error analysis into random error, reading error and calibration error, and supplies worksheets for determining them
- Provides a set of linked examples showing how error analysis impacts various aspects of a complex problem
- Gives specific instructions for carrying out error analysis using both Excel and MATLAB®
Publication planned for: January 2021
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Practical Philosophy from Kant to Hegel Freedom, Right, and Revolution
Scholarship on Kant's practical philosophy has often overlooked its
reception in the early days of post-Kantian philosophy and German
Idealism. This volume of new essays illuminates that reception and how
it informed the development of practical philosophy between Kant and
Hegel...
Taken together, the essays provide an historically informed and
philosophically nuanced picture of the development of post-Kantian
practical philosophy.
- Illuminates the reception of Kant's practical philosophy
- Highlights the importance of lesser-known figures in the German philosophical tradition such as Erhard and Reimarus
- Explores important topics related to right, morality, freedom and revolution
Publication planned for: March 2021
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Happy reading 📚books and drink ☕️coffee!
Source: Cambridge University Press.