Hodnett / Wiley R Deep Learning Essentials

A step-by-step guide to building deep learning models using TensorFlow, Keras, and MXNet
2. Auflage 2018
ISBN: 978-1-78899-780-5
Verlag: De Gruyter
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 378 Seiten

No detailed description available for "R Deep Learning Essentials".

Autoren/Hrsg.

Hodnett, Mark

Wiley, Joshua F.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Spracherkennung, Sprachverarbeitung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Neuronale Netzwerke

Inhaltsverzeichnis

Table of Contents - Getting Started with Deep Learning
- Training a Prediction Model
- Deep Learning Fundamentals
- Training Deep Prediction Models
- Image Classification Using Convolutional Neural Networks
- Tuning and Optimizing Models
- Natural Language Processing Using Deep Learning
- Deep Learning Models Using TensorFlow in R
- Anomaly Detection and Recommendation Systems
- Running Deep Learning Models in the Cloud
- The Next Level in Deep Learning

Über Autor(innen)

Hodnett Mark:

Mark Hodnett is a data scientist with over 20 years of industry experience in software development, business intelligence systems, and data science. He has worked in a variety of industries, including CRM systems, retail loyalty, IoT systems, and accountancy. He holds a master's in data science and an MBA. He works in Cork, Ireland, as a senior data scientist with AltViz.Wiley Joshua F.:

Joshua F. Wiley is a lecturer at Monash University, conducting quantitative research on sleep, stress, and health. He earned his Ph.D. from the University of California, Los Angeles and completed postdoctoral training in primary care and prevention. In statistics and data science, Joshua focuses on biostatistics and is interested in reproducible research and graphical displays of data and statistical models. He develops or co-develops a number of R packages including Varian, a package to conduct Bayesian scale-location structural equation models, and MplusAutomation, a popular package that links R to the commercial Mplus software.