Описание: Examine the latest technological advancements in building a scalable machine-learning model with big data using R. This second edition shows you how to work with a machine-learning algorithm and use it to build a ML model from raw data. You will see how to use R programming with TensorFlow, thus avoiding the effort of learning Python if you are only comfortable with R.As in the first edition, the authors have kept the fine balance of theory and application of machine learning through various real-world use-cases which gives you a comprehensive collection of topics in machine learning. New chapters in this edition cover time series models and deep learning.What You'll Learn Understand machine learning algorithms using RMaster the process of building machine-learning models Cover the theoretical foundations of machine-learning algorithmsSee industry focused real-world use casesTackle time series modeling in RApply deep learning using Keras and TensorFlow in RWho This Book is ForData scientists, data science professionals, and researchers in academia who want to understand the nuances of machine-learning approaches/algorithms in practice using R.
Автор: Karthik S., Paul Anand, Karthikeyan N. Название: Deep Learning Innovations and Their Convergence with Big Data ISBN: 1522530150 ISBN-13(EAN): 9781522530152 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 29938.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The expansion of digital data has transformed various sectors of business such as healthcare, industrial manufacturing, and transportation. A new way of solving business problems has emerged through the use of machine learning techniques in conjunction with big data analytics.Deep Learning Innovations and Their Convergence With Big Data is a pivotal reference for the latest scholarly research on upcoming trends in data analytics and potential technologies that will facilitate insight in various domains of science, industry, business, and consumer applications. Featuring extensive coverage on a broad range of topics and perspectives such as deep neural network, domain adaptation modeling, and threat detection, this book is ideally designed for researchers, professionals, and students seeking current research on the latest trends in the field of deep learning techniques in big data analytics.Contents include:Deep Auto-EncodersDeep Neural NetworkDomain Adaptation ModelingMultilayer Perceptron (MLP)Natural Language Processing (NLP)Restricted Boltzmann Machines (RBM)Threat Detection
Описание: Prudent, verifiable, and timely corporate accounting is a bedrock of our modern capitalist system. In recent years, however, the rules that govern corporate accounting have been subtly changed in ways that compromise these core principles, to the detriment of the economy at large. These changes have been driven by the private agendas of certain corporate special interests, aided selectively--and sometimes unwittingly--by arguments from business academia With Political Standards, Karthik Ramanna develops the notion of "thin political markets" to describe a key problem facing technical rule-making in corporate accounting and beyond. When standard-setting boards attempt to regulate the accounting practices of corporations, they must draw on a small pool of qualified experts--but those experts almost always have strong commercial interests in the outcome. Meanwhile, standard setting rarely enjoys much attention from the general public. This absence of accountability, Ramanna argues, allows corporate managers to game the system. In the profit-maximization framework of modern capitalism, the only practicable solution is to reframe managerial norms when participating in thin political markets. Political Standards will be an essential resource for understanding how the rules of the game are set, whom they inevitably favor, and how the process can be changed for a better capitalism.
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