Fundraising September 15, 2024 – October 1, 2024 About fundraising

Optimization for Data Analysis

Main
Computers - Algorithms and Data Structures
Optimization for Data Analysis

Optimization for Data Analysis

Stephen J. Wright, Benjamin Recht

1.0 / 1.0

How much do you like this book?

What’s the quality of the file?

Download the book for quality assessment

What’s the quality of the downloaded files?

A concise text that presents and analyzes the fundamental techniques and methods in optimization that are useful in data science.

Categories:

Computers - Algorithms and Data Structures

Year:

2022

Publisher:

Cambridge University Press

Language:

english

Pages:

239

ISBN 10:

1316518981

ISBN 13:

9781009004282

File:

PDF, 3.76 MB

IPFS:

,

english, 2022

Read Online

Conversion to is in progress

Conversion to is failed

You may be interested in

Ville Tuulos — Effective Data Science Infrastructure - MEAP Version 7

John Wright, Yi Ma — High-Dimensional Data Analysis with Low-Dimensional Models: Principles, Computation, and Applications

Vickler, Andy — R Programming: R Data Analysis and Statistics

Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann — Data Visualization: Exploring and Explaining with Data

Dan Stanescu, Long Lee — A Gentle Introduction to Scientific Computing

Hasmik Osipyan, Bosede Edwards, Adrian David Cheok — Deep Neural Network Applications

Miklos Bona — Combinatorics of Permutations (Discrete Mathematics and Its Applications), 3rd Edition

Nathan Taback — Design and Analysis of Experiments and Observational Studies Using R

Uwe Engel, Anabel Quan-Haase, Sunny Liu, Lars Lyberg — Handbook of Computational Social Science, Volume 2: Data Science, Statistical Modelling, and Machine Learning Methods

Timothy R. Anderson — Optimization Modeling Using R

Brandon M. Greenwell — Tree-Based Methods for Statistical Learning in R: A Practical Introduction with Applications in R

Niyati Aggrawal, Adarsh Anand — Social Networks: Modelling and Analysis

Amit Kumar Tyagi; — Data Science and Data Analytics

Leonard J. Savage — The Foundations of Statistics, Second Revised Edition

Viktor Mayer-Schonberger, Thomas Ramge — Access Rules: Freeing Data from Big Tech for a Better Future

Tamal Krishna Dey, Yusu Wang — Computational Topology for Data Analysis

Joseph O'Rourke — Pop-Up Geometry: The Mathematics Behind Pop-Up Card

Punit Gupta, Mayank Kumar Goyal, Sudeshna Chakraborty, Ahmed A. Elngar — Machine Learning and Optimization Models for Optimization in Cloud

Tomas Beuzen, Tiffany-Anne Timbers — Python Packages (Chapman & Hall/CRC The Python Series)

Michael Kennedy, Matt Harrison — Effective PyCharm: Learn the PyCharm IDE with a Hands-on Approach

Bonny P. McClain — Python for Geospatial Data Analysis (First Early Release)

Victor Lee, Xinyu Chang, Gaurav Deshpande — Graph-Powered Analytics and Machine Learning with TigerGraph

Mike Amundsen — Restful Web API Patterns and Practices Cookbook (Early Fourth Release)

Jacqueline Nolis — Leading Data Science Teams

Giuseppe C. Calafiore, Laurent El Ghaoui — Optimization Models

Philipp Hennig, Michael A. Osborne, Hans P. Kersting — Probabilistic Numerics: Computation as Machine Learning

Domínguez-García, Alejandro D. — Large-Scale System Analysis Under Uncertainty: With Electric Power Applications

Jose Manuel Magallanes Reyes — Introduction to Data Science for Social and Policy Research: Collecting and Organizing Data with R and Python

Richard Ansorge — Programming in Parallel with CUDA: A Practical Guide

Gilles Barthe, Joost-Pieter Katoen, Alexandra Silva — Foundations of Probabilistic Programming

Bernhard Mehlig — Machine Learning with Neural Networks: An Introduction for Scientists and Engineers

Jose Manuel Magallanes Reyes — Data Visualization for Social and Policy Research: A Step-by-Step Approach Using R and Python

Gábor Békés, Gábor Kézdi — Data Analysis for Business, Economics, and Policy

Walter W. Piegorsch, Richard F. Levine, Hao Helen Zhang, Thomas C. M. Lee — Handbook of Computational Statistics and Data Science

Amir Sadr — Mathematical Techniques in Finance: An Introduction (Wiley Finance)

Liao Heng, Bill McColl — Mathematics for Future Computing and Communications

Simon Foucart — Mathematical Pictures at a Data Science Exhibition

Julian McAuley — Personalized Machine Learning

Daniel A. Roberts, Sho Yaida, Boris Hanin — The Principles of Deep Learning Theory

Henry D. I. Abarbanel — The Statistical Physics of Data Assimilation and Machine Learning

John Vince — Mathematics for Computer Graphics (Undergraduate Topics in Computer Science), 6th Edition 2022

Paula Brito, Sónia Dias, (editors) — Analysis of Distributional Data

Most frequently terms