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Book synopsis

A concise and practical exploration of key topics and applications in data scienceIn Deep Learning, from Big Data to Artificial Intelligence, 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 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 problemsPerfect for graduate students studying data science, big data, deep learning, and artificial intelligence, Deep Learning, from Big Data to Artificial Intelligence will also earn a place in the libraries of data science researchers and practicing data scientists.

Book details

• Full title: Deep Learning - From Big Data to Artificial Intelligence with R
• Author: Stephane S. Tuffery
• Language: English
• Binding: Hardback
• Number of pages: 544
• EAN: 9781119845010
• ISBN: 1119845017
• ID: 38795077
• Publisher: John Wiley and Sons Ltd
• Weight: 1100 g
• Dimensions: 177 × 250 × 36 mm
• Date of publishing: 08. December 2022

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