Neural Data Science

• Author : Erik Lee Nylen,Pascal Wallisch
• Publisher : Academic Press
• Release Date : 2017-02-24
• ISBN : 9780128040980

A Primer with MATLAB® and PythonTM present important information on the emergence of the use of Python, a more general purpose option to MATLAB, the preferred computation language for scientific computing and analysis in neuroscience. This book addresses the snake in the room by providing a beginner’s introduction to the principles of computation and data analysis in neuroscience, using both Python and MATLAB, giving readers the ability to transcend platform tribalism and enable coding versatility. Includes discussions of both

• Author : Robert E. Kass,Uri T. Eden,Emery N. Brown
• Publisher : Springer
• Release Date : 2014-07-08
• ISBN : 9781461496021

Continual improvements in data collection and processing have had a huge impact on brain research, producing data sets that are often large and complicated. By emphasizing a few fundamental principles, and a handful of ubiquitous techniques, Analysis of Neural Data provides a unified treatment of analytical methods that have become essential for contemporary researchers. Throughout the book ideas are illustrated with more than 100 examples drawn from the literature, ranging from electrophysiology, to neuroimaging, to behavior. By demonstrating the commonality among

• Author : Mark A. Kramer,Uri T. Eden
• Publisher : MIT Press
• Release Date : 2016-11-04
• ISBN : 9780262529372

A practical guide to neural data analysis techniques that presents sample datasets and hands-on methods for analyzing the data. As neural data becomes increasingly complex, neuroscientists now require skills in computer programming, statistics, and data analysis. This book teaches practical neural data analysis techniques by presenting example datasets and developing techniques and tools for analyzing them. Each chapter begins with a specific example of neural data, which motivates mathematical and statistical analysis methods that are then applied to the data.

• Author : Erik Lee Nylen,Pascal Wallisch
• Publisher : Academic Press
• Release Date : 2017-02-01
• ISBN : 0128040432

MATLAB remains the dominant language for scientific computing and analysis in neuroscience, but a more general purpose option - Python - is emerging. This book addresses the snake in the room by providing a beginner s introduction to the principles of computation and data analysis in neuroscience using both Python and MATLAB, which allows to transcend platform tribalism and enables coding versatility. Utilizing a Rosetta stone approach, including both MATLAB and Python in parallel Introducing the canonical data analysis cascade,

• Author : Antonio Canale,Daniele Durante,Lucia Paci,Bruno Scarpa
• Publisher : Springer
• Release Date : 2018-12-28
• ISBN : 9783030000394

This volume presents a collection of peer-reviewed contributions arising from StartUp Research: a stimulating research experience in which twenty-eight early-career researchers collaborated with seven senior international professors in order to develop novel statistical methods for complex brain imaging data. During this meeting, which was held on June 25–27, 2017 in Siena (Italy), the research groups focused on recent multimodality imaging datasets measuring brain function and structure, and proposed a wide variety of methods for network analysis, spatial inference, graphical modeling, multiple testing,

• Author : Pascal Wallisch,Michael E. Lusignan,Marc D. Benayoun,Tanya I. Baker,Adam Seth Dickey,Nicholas G. Hatsopoulos
• Publisher : Academic Press
• Release Date : 2014-01-09
• ISBN : 9780123838377

MATLAB for Neuroscientists serves as the only complete study manual and teaching resource for MATLAB, the globally accepted standard for scientific computing, in the neurosciences and psychology. This unique introduction can be used to learn the entire empirical and experimental process (including stimulus generation, experimental control, data collection, data analysis, modeling, and more), and the 2nd Edition continues to ensure that a wide variety of computational problems can be addressed in a single programming environment. This updated edition features additional

• Author : Joel Grus
• Publisher : "O'Reilly Media, Inc."
• Release Date : 2015-04-14
• ISBN : 9781491904404

Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core

• Author : James D. Miller
• Publisher : Packt Publishing Ltd
• Release Date : 2017-11-17
• ISBN : 9781788295345

Get your statistics basics right before diving into the world of data science About This Book No need to take a degree in statistics, read this book and get a strong statistics base for data science and real-world programs; Implement statistics in data science tasks such as data cleaning, mining, and analysis Learn all about probability, statistics, numerical computations, and more with the help of R programs Who This Book Is For This book is intended for those developers who

• Author : Riley Adams,Matt Henderson
• Publisher : This Is Charlotte.
• Release Date : 2019-05-12
• ISBN : 199917707X

★This book includes 2 Manuscripts★ Are you looking for new ways to grow your business, with resources you already have? Do you want to know how the big players like Netflix, Amazon, or Shopify use data analytics to MULTIPLY their growth? Keep listening to learn how to use data analytics to maximize YOUR business.

• Author : Gintautas Dzemyda,Jolita Bernatavičienė,Janusz Kacprzyk
• Publisher : Springer Nature
• Release Date : 2020-02-13
• ISBN : 9783030392505

This book contains 16 chapters by researchers working in various fields of data science. They focus on theory and applications in language technologies, optimization, computational thinking, intelligent decision support systems, decomposition of signals, model-driven development methodologies, interoperability of enterprise applications, anomaly detection in financial markets, 3D virtual reality, monitoring of environmental data, convolutional neural networks, knowledge storage, data stream classification, and security in social networking. The respective papers highlight a wealth of issues in, and applications of, data science. Modern technologies

• Author : Ilke Demir,Yifei Lou,Xu Wang,Kathrin Welker
• Publisher : Springer
• Release Date : 2021-11-30
• ISBN : 3030798909

This volume highlights recent advances in data science, including image processing and enhancement on large data, shape analysis and geometry processing in 2D/3D, exploration and understanding of neural networks, and extensions to atypical data types such as social and biological signals. The contributions are based on discussions from two workshops under Association for Women in Mathematics (AWM), namely the second Women in Data Science and Mathematics (WiSDM) Research Collaboration Workshop that took place between July 29 and August 2, 2019 at the

• Author : Jalil Villalobos Alva
• Publisher : Apress
• Release Date : 2021-03-28
• ISBN : 1484265939

Enhance your data science programming and analysis with the Wolfram programming language and Mathematica, an applied mathematical tools suite. The book introduces you to the Wolfram programming language and its syntax, as well as the structure of Mathematica and its advantages and disadvantages. You’ll see how to use the Wolfram language for data science from a theoretical and practical perspective. Learning this language makes your data science code better because it is very intuitive and comes with pre-existing functions

• Author : Basilio de Braganca Pereira,Calyampudi Radhakrishna Rao,Fabio Borges de Oliveira
• Publisher : CRC Press
• Release Date : 2020-08-25
• ISBN : 9780429775543

Statistical Learning using Neural Networks: A Guide for Statisticians and Data Scientists with Python introduces artificial neural networks starting from the basics and increasingly demanding more effort from readers, who can learn the theory and its applications in statistical methods with concrete Python code examples. It presents a wide range of widely used statistical methodologies, applied in several research areas with Python code examples, which are available online. It is suitable for scientists and developers as well as graduate students.

• Author : Vijay Kotu,Bala Deshpande
• Publisher : Morgan Kaufmann
• Release Date : 2018-11-27
• ISBN : 9780128147627

Learn the basics of Data Science through an easy to understand conceptual framework and immediately practice using RapidMiner platform. Whether you are brand new to data science or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Science has become an essential tool to extract value from data for any organization that collects, stores and processes data as part of its operations. This

• Author : Aboul-Ella Hassanien,Mohamed Hamed N. Taha,Nour Eldeen M. Khalifa
• Publisher : Springer Nature
• Release Date : 2020-09-23
• ISBN : 9783030520670

This book provides a detailed overview of the latest developments and applications in the field of artificial intelligence and data science. AI applications have achieved great accuracy and performance with the help of developments in data processing and storage. It has also gained strength through the amount and quality of data which is the main nucleus of data science. This book aims to provide the latest research findings in the field of artificial intelligence with data science.