Deep Learning: From Big Data to Artificial Intelligence with R

Big data and deep learning are not just abstract technical ideas: they shape work in fields from finance and healthcare to industry and scientific research. In Deep Learning: From Big Data to Artificial Intelligence with R, Stéphane Tufféry builds a broad, practical account of these technologies, linking their foundations to real applications and illustrating methods with R and other major deep-learning libraries.

The book also gives attention to the limits and responsibilities of data-driven work. Alongside algorithms and applications, it considers data quality, overfitting, explainability, and the protection of personal information—issues that matter whenever models are put to use beyond the page.

From large datasets to workable methods

The opening chapters establish the landscape: how big data is used across organizations and everyday life, what can go wrong in data processing, and how large volumes of information can be handled. Topics include distributed and parallel computing, tools such as Hadoop and Spark, computing resources, and the roles of R and Python.

Machine learning foundations, then deeper applications

Readers get a grounding in optimization and established machine-learning approaches, including ensemble methods, random forests, boosting, support vector machines, and recommendation systems. The later chapters extend that base into natural language processing, social network analysis, handwriting recognition, deep learning, computer vision, and artificial intelligence.

Examples across disciplines

Applications discussed in the book range from finance, insurance, and industry to healthcare, education, transport, and scientific research. This breadth helps place technical methods in context: readers can consider not only how data and models are processed, but also where they are used and what risks deserve attention.

Designed for study and practice

The material combines conceptual discussion with practical instruction involving R and deep-learning libraries including MXNet, PyTorch, and Keras-TensorFlow. The book describes companion R and Python source code for its examples, making it a useful reference point for readers studying data science or exploring applied deep-learning methods.

With its progression from big-data fundamentals to specialized applications, this book will interest graduate students, researchers, and data-science practitioners looking for a wide-ranging treatment of deep learning through an R-centered lens.