| Description |
1 online resource (542 p.) |
| Contents |
Cover -- Title Page -- Copyright -- Contents -- Acknowledgements -- Introduction -- Chapter 1 From Big Data to Deep Learning -- 1.1 Introduction -- 1.2 Examples of the Use of Big Data and Deep Learning -- 1.3 Big Data and Deep Learning for Companies and Organizations -- 1.3.1 Big Data in Finance -- 1.3.1.1 Google Trends -- 1.3.1.2 Google Trends and Stock Prices -- 1.3.1.3 The quantmod Package for Financial Analysis -- 1.3.1.4 Google Trends in R -- 1.3.1.5 Matching Data from quantmod and Google Trends -- 1.3.2 Big Data and Deep Learning in Insurance |
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1.3.3 Big Data and Deep Learning in Industry -- 1.3.4 Big Data and Deep Learning in Scientific Research and Education -- 1.3.4.1 Big Data in Physics and Astrophysics -- 1.3.4.2 Big Data in Climatology and Earth Sciences -- 1.3.4.3 Big Data in Education -- 1.4 Big Data and Deep Learning for Individuals -- 1.4.1 Big Data and Deep Learning in Healthcare -- 1.4.1.1 Connected Health and Telemedicine -- 1.4.1.2 Geolocation and Health -- 1.4.1.3 The Google Flu Trends -- 1.4.1.4 Research in Health and Medicine -- 1.4.2 Big Data and Deep Learning for Drivers |
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1.4.3 Big Data and Deep Learning for Citizens -- 1.4.4 Big Data and Deep Learning in the Police -- 1.5 Risks in Data Processing -- 1.5.1 Insufficient Quantity of Training Data -- 1.5.2 Poor Data Quality -- 1.5.3 Non-Representative Samples -- 1.5.4 Missing Values in the Data -- 1.5.5 Spurious Correlations -- 1.5.6 Overfitting -- 1.5.7 Lack of Explainability of Models -- 1.6 Protection of Personal Data -- 1.6.1 The Need for Data Protection -- 1.6.2 Data Anonymization -- 1.6.3 The General Data Protection Regulation -- 1.7 Open Data -- Notes -- Chapter 2 Processing of Large Volumes of Data |
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2.1 Issues -- 2.2 The Search for a Parsimonious Model -- 2.3 Algorithmic Complexity -- 2.4 Parallel Computing -- 2.5 Distributed Computing -- 2.5.1 MapReduce -- 2.5.2 Hadoop -- 2.5.3 Computing Tools for Distributed Computing -- 2.5.4 Column-Oriented Databases -- 2.5.5 Distributed Architecture and "Analytics" -- 2.5.6 Spark -- 2.6 Computer Resources -- 2.6.1 Minimum Resources -- 2.6.2 Graphics Processing Units (GPU) and Tensor Processing Units (TPU) -- 2.6.3 Solutions in the Cloud -- 2.7 R and Python Software -- 2.8 Quantum Computing -- Notes -- Chapter 3 Reminders of Machine Learning |
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3.1 General -- 3.2 The Optimization Algorithms -- 3.3 Complexity Reduction and Penalized Regression -- 3.4 Ensemble Methods -- 3.4.1 Bagging -- 3.4.2 Random Forests -- 3.4.3 Extra-Trees -- 3.4.4 Boosting -- 3.4.5 Gradient Boosting Methods -- 3.4.6 Synthesis of the Ensemble Methods -- 3.5 Support Vector Machines -- 3.6 Recommendation Systems -- Notes -- Chapter 4 Natural Language Processing -- 4.1 From Lexical Statistics to Natural Language Processing -- 4.2 Uses of Text Mining and Natural Language Processing -- 4.3 The Operations of Textual Analysis -- 4.3.1 Textual Data Collection |
| Summary |
In Deep Learning: From Big Data to Artificial Intelligence with R, expert researcher Dr. Stéphane Tufféry delivers an insightful discussion of the applications of deep learning and big data that focuses on practical instructions on various software tools and deep learning methods relying on three major libraries: MXNet, PyTorch, and Keras-TensorFlow. In the book, numerous, up-to-date examples are combined with key topics relevant to modern data scientists, including processing optimization, neural network applications, natural language processing, and image recognition.This is a thoroughly revised and updated edition of a book originally released in French, with new examples and methods included throughout. Classroom-tested and intuitively organized, Deep Learning: From Big Data to Artificial Intelligence with R offers complimentary access to a companion website that provides R and Python source code for the examples offered in the book. Readers will also find:- A thorough introduction to practical deep learning techniques with explanations and examples for various programming libraries- Comprehensive explorations of a variety of applications for deep learning, including image recognition and natural language processing- Discussions of the theory of deep learning, neural networks, and artificial intelligence linked to concrete techniques and strategies commonly used to solve real-world problems |
| Notes |
Description based upon print version of record |
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4.3.2 Identification of the Language |
| Form |
Electronic book
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| ISBN |
9781119845027 |
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1119845025 |
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1119845041 |
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9781119845041 |
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