Neural Data Science
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- Author : Erik Lee Nylen
- Publisher : Academic Press
- Release : 24 February 2017
- ISBN : 9780128040980
- Page : 368 pages
- Rating : 4.5/5 from 103 voters
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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 MATLAB and Python in parallel Introduces the canonical data analysis cascade, standardizing the data analysis flow Presents tactics that strategically, tactically, and algorithmically help improve the organization of code
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
Analysis of Neural Data
- 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
Neural Data Science
- 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,
Studies in Neural Data Science
- 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,
Statistics for Data Science
- 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
Case Studies in Neural Data Analysis
- Author : Mark A. Kramer,Uri T. Eden
- Publisher : MIT Press
- Release Date : 2016-10-28
- ISBN : 9780262336741
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.
Data Science from Scratch
- 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
Analyzing Neural Time Series Data
- Author : Mike X Cohen
- Publisher : MIT Press
- Release Date : 2014-01-17
- ISBN : 9780262019873
A comprehensive guide to the conceptual, mathematical, and implementational aspects of analyzing electrical brain signals, including data from MEG, EEG, and LFP recordings. This book offers a comprehensive guide to the theory and practice of analyzing electrical brain signals. It explains the conceptual, mathematical, and implementational (via Matlab programming) aspects of time-, time-frequency- and synchronization-based analyses of magnetoencephalography (MEG), electroencephalography (EEG), and local field potential (LFP) recordings from humans and nonhuman animals. It is the only book on the topic
Data Science for Business 2019 (2 BOOKS IN 1)
- 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.
Malware Data Science
- Author : Joshua Saxe,Hillary Sanders
- Publisher : No Starch Press
- Release Date : 2018-09-25
- ISBN : 9781593278595
Malware Data Science explains how to identify, analyze, and classify large-scale malware using machine learning and data visualization. Security has become a "big data" problem. The growth rate of malware has accelerated to tens of millions of new files per year while our networks generate an ever-larger flood of security-relevant data each day. In order to defend against these advanced attacks, you'll need to know how to think like a data scientist. In Malware Data Science, security data scientist Joshua
MATLAB for Neuroscientists
- 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
Data Science
- 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
Statistical Signal Processing for Neuroscience and Neurotechnology
- Author : Karim G. Oweiss
- Publisher : Academic Press
- Release Date : 2010-09-22
- ISBN : 0080962963
This is a uniquely comprehensive reference that summarizes the state of the art of signal processing theory and techniques for solving emerging problems in neuroscience, and which clearly presents new theory, algorithms, software and hardware tools that are specifically tailored to the nature of the neurobiological environment. It gives a broad overview of the basic principles, theories and methods in statistical signal processing for basic and applied neuroscience problems. Written by experts in the field, the book is an ideal
Data-Driven Computational Neuroscience
- Author : Concha Bielza,Pedro Larrañaga
- Publisher : Cambridge University Press
- Release Date : 2020-11-26
- ISBN : 9781108493703
Trains researchers and graduate students in state-of-the-art statistical and machine learning methods to build models with real-world data.
Intelligent Techniques for Data Science
- Author : Rajendra Akerkar,Priti Srinivas Sajja
- Publisher : Springer
- Release Date : 2016-10-11
- ISBN : 9783319292069
This textbook provides readers with the tools, techniques and cases required to excel with modern artificial intelligence methods. These embrace the family of neural networks, fuzzy systems and evolutionary computing in addition to other fields within machine learning, and will help in identifying, visualizing, classifying and analyzing data to support business decisions./p> The authors, discuss advantages and drawbacks of different approaches, and present a sound foundation for the reader to design and implement data analytic solutions for real‐world