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Realtime Data Mining: Self-Learning Techniques for Recommendation Engines (Applied and Numerical Harmonic Analysis)
Realtime Data Mining: Self-Learning Techniques for Recommendation Engines (Applied and Numerical Harmonic Analysis)

​​​​Describing novel mathematical concepts for recommendation engines, Realtime Data Mining: Self-Learning Techniques for Recommendation Engines features a sound mathematical framework unifying approaches based on control and learning theories, tensor factorization, and hierarchical methods. Furthermore, it...

Machine Learning Projects for .NET Developers
Machine Learning Projects for .NET Developers

Machine Learning Projects for .NET Developers shows you how to build smarter .NET applications that learn from data, using simple algorithms and techniques that can be applied to a wide range of real-world problems. You’ll code each project in the familiar setting of Visual Studio, while the machine learning logic uses F#, a...

Advanced Engineering Mathematics
Advanced Engineering Mathematics

Advanced Engineering Mathematics provides comprehensive and contemporary coverage of key mathematical ideas, techniques, and their widespread applications, for students majoring in engineering, computer science, mathematics and physics. Using a wide range of examples throughout the book, Jeffrey illustrates how to construct simple...

Advanced Calculus: An Introduction to Mathematical Analysis
Advanced Calculus: An Introduction to Mathematical Analysis

This work is an introduction to mathematical analysis at an elementary level. Emphasis is given to the construction of national and then real numbers, using the method of equivalence classes and of Cauchy sequences. The text includes the presentation of: sequences of real numbers, infinite numerical series, continuous functions, deriviatives...

Introduction to Linear Algebra, Third Edition
Introduction to Linear Algebra, Third Edition

This informally written text provides students with a clear introduction into the subject of linear algebra. Topics covered include matrix multiplication, row reduction, matrix inverse, orthogonality and computation. The self-teaching book is loaded with examples and graphics and provides a wide array of probing problems, accompanying...

C++ Standard Library Practical Tips (Programming Series)
C++ Standard Library Practical Tips (Programming Series)
Put the Power of the C++ Standard Library to Work!

C++ Standard Library Practical Tips teaches beginning and experienced programmers how to use the Standard Library and its major component the Standard Template Library (STL) effectively in routine programming chores. The book provides 100 quick, easy-to-use tips and solutions to common...

Mastering Parallel Programming with R
Mastering Parallel Programming with R

Master the robust features of R parallel programming to accelerate your data science computations

About This Book

  • Create R programs that exploit the computational capability of your cloud platforms and computers to the fullest
  • Become an expert in writing the most efficient and highest...
Continuous-Time Markov Chains and Applications: A Two-Time-Scale Approach (Stochastic Modelling and Applied Probability)
Continuous-Time Markov Chains and Applications: A Two-Time-Scale Approach (Stochastic Modelling and Applied Probability)

This book gives a systematic treatment of singularly perturbed systems that naturally arise in control and optimization, queueing networks, manufacturing systems, and financial engineering. It presents results on asymptotic expansions of solutions of Komogorov forward and backward equations, properties of functional occupation measures,...

Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib
Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib

Leverage the numerical and mathematical modules in Python and its standard library as well as popular open source numerical Python packages like NumPy, SciPy, FiPy, matplotlib and more. This fully revised edition, updated with the latest details of each package and changes to Jupyter projects, demonstrates how to numerically compute...

Numerical Computing with Python: Harness the power of Python to analyze and find hidden patterns in the data
Numerical Computing with Python: Harness the power of Python to analyze and find hidden patterns in the data

Understand, explore, and effectively present data using the powerful data visualization techniques of Python

Key Features

  • Use the power of Pandas and Matplotlib to easily solve data mining issues
  • Understand the basics of statistics to build powerful predictive data...
Introduction to Partial Differential Equations (Undergraduate Texts in Mathematics)
Introduction to Partial Differential Equations (Undergraduate Texts in Mathematics)

This textbook is designed for a one year course covering the fundamentals of partial differential equations, geared towards advanced undergraduates and beginning graduate students in mathematics, science, engineering, and elsewhere. The exposition carefully balances solution techniques, mathematical rigor, and significant applications, all...

Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas,...

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