Neural Data Science

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

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  • Author : Erik Lee Nylen
  • Publisher : Academic Press
  • Pages : 368 pages
  • ISBN : 012804098X
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKNeural Data Science

Neural Data Science

Neural Data Science
  • Author : Erik Lee Nylen,Pascal Wallisch
  • Publisher : Academic Press
  • Release : 24 February 2017
GET THIS BOOKNeural Data Science

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

Neural Data Science

Neural Data Science
  • Author : Erik Lee Nylen,Pascal Wallisch
  • Publisher : Academic Press
  • Release : 01 February 2017
GET THIS BOOKNeural Data Science

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,

Analysis of Neural Data

Analysis of Neural Data
  • Author : Robert E. Kass,Uri T. Eden,Emery N. Brown
  • Publisher : Springer
  • Release : 08 July 2014
GET THIS BOOKAnalysis of Neural Data

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

Studies in Neural Data Science

Studies in Neural Data Science
  • Author : Antonio Canale,Daniele Durante,Lucia Paci,Bruno Scarpa
  • Publisher : Springer
  • Release : 28 December 2018
GET THIS BOOKStudies in Neural Data Science

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

Statistics for Data Science
  • Author : James D. Miller
  • Publisher : Packt Publishing Ltd
  • Release : 17 November 2017
GET THIS BOOKStatistics for Data Science

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

Malware Data Science

Malware Data Science
  • Author : Joshua Saxe,Hillary Sanders
  • Publisher : No Starch Press
  • Release : 25 September 2018
GET THIS BOOKMalware Data Science

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

Data Science from Scratch

Data Science from Scratch
  • Author : Joel Grus
  • Publisher : "O'Reilly Media, Inc."
  • Release : 14 April 2015
GET THIS BOOKData Science from Scratch

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

Data Science

Data Science
  • Author : Vijay Kotu,Bala Deshpande
  • Publisher : Morgan Kaufmann
  • Release : 27 November 2018
GET THIS BOOKData Science

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

Data Science for Business 2019 (2 BOOKS IN 1)

Data Science for Business 2019 (2 BOOKS IN 1)
  • Author : Riley Adams,Matt Henderson
  • Publisher : This Is Charlotte.
  • Release : 12 May 2019
GET THIS BOOKData Science for Business 2019 (2 BOOKS IN 1)

★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.

MATLAB for Neuroscientists

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 : 09 January 2014
GET THIS BOOKMATLAB for Neuroscientists

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

Case Studies in Neural Data Analysis

Case Studies in Neural Data Analysis
  • Author : Mark A. Kramer,Uri T. Eden
  • Publisher : MIT Press
  • Release : 28 October 2016
GET THIS BOOKCase Studies in Neural Data Analysis

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.

Machine Learning and Data Science in the Oil and Gas Industry

Machine Learning and Data Science in the Oil and Gas Industry
  • Author : Patrick Bangert
  • Publisher : Gulf Professional Publishing
  • Release : 04 March 2021
GET THIS BOOKMachine Learning and Data Science in the Oil and Gas Industry

Machine Learning and Data Science in the Oil and Gas Industry explains how machine learning can be specifically tailored to oil and gas use cases. Petroleum engineers will learn when to use machine learning, how it is already used in oil and gas operations, and how to manage the data stream moving forward. Practical in its approach, the book explains all aspects of a data science or machine learning project, including the managerial parts of it that are so often

Data Science Solutions with Python

Data Science Solutions with Python
  • Author : Tshepo Chris Nokeri
  • Publisher : Apress
  • Release : 26 October 2021
GET THIS BOOKData Science Solutions with Python

Apply supervised and unsupervised learning to solve practical and real-world big data problems. This book teaches you how to engineer features, optimize hyperparameters, train and test models, develop pipelines, and automate the machine learning (ML) process. The book covers an in-memory, distributed cluster computing framework known as PySpark, machine learning framework platforms known as scikit-learn, PySpark MLlib, H2O, and XGBoost, and a deep learning (DL) framework known as Keras. The book starts off presenting supervised and unsupervised ML and