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Machine Learning Using R: With Time Series and Industry-Based Use Cases in R, Ramasubramanian Karthik, Singh Abhishek


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Автор: Ramasubramanian Karthik, Singh Abhishek
Название:  Machine Learning Using R: With Time Series and Industry-Based Use Cases in R
ISBN: 9781484242148
Издательство: Springer
Классификация:





ISBN-10: 1484242149
Обложка/Формат: Paperback
Страницы: 700
Вес: 1.24 кг.
Дата издания: 13.12.2018
Язык: English
Издание: 2nd ed.
Иллюстрации: 24 illustrations, color; 209 illustrations, black and white; xxiv, 700 p. 233 illus., 24 illus. in color.
Размер: 266 x 186 x 44
Читательская аудитория: Professional & vocational
Подзаголовок: With time series and industry-based use cases in r
Ссылка на Издательство: Link
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Поставляется из: Германии
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 Youll 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.
Дополнительное описание:
Chapter 1: Introduction to Machine Learning.- Chapter 2: Data Exploration and Preparation.- Chapter 3: Sampling and Resampling Techniques.- Chapter 4: Visualization of Data.- Chapter 5: Feature Engineering.- Chapter 6: Machine Learning Models: Theo



Deep Learning Innovations and Their Convergence with Big Data

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)
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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


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