Описание: Blockchain is a technology that transcends cryptocurrencies. There are other services in different sectors of the economy that can benefit from the trust and security that blockchains offer. For example, financial institutions are using blockchains for international money transfer, and in logistics, it has been used for supply chain management and tracking of goods. As more global companies and governments are experimenting and deploying blockchain solutions, it is necessary to compile knowledge on the best practices, strategies, and failures in order to create a better awareness of how blockchain could either support or add value to other services.
Cross-Industry Use of Blockchain Technology and Opportunities for the Future provides emerging research highlighting the possibilities inherent in blockchain for different sectors of the economy and the added value blockchain can provide for the future of these different sectors. Featuring coverage on a broad range of topics such as data privacy, information sharing, and digital identity, this book is ideally designed for IT specialists, consultants, design engineers, cryptographers, service designers, researchers, academics, government officials, and industry professionals.
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.
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