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OverviewFull Product DetailsAuthor: Mark Hodnett , Joshua F. Wiley , Yuxi (Hayden) Liu , Pablo MaldonadoPublisher: Packt Publishing Limited Imprint: Packt Publishing Limited ISBN: 9781838642709ISBN 10: 1838642706 Pages: 612 Publication Date: 20 May 2019 Audience: General/trade , General Format: Paperback Publisher's Status: Active Availability: In stock  We have confirmation that this item is in stock with the supplier. It will be ordered in for you and dispatched immediately. Table of ContentsTable 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 Handwritten Digit Recognition Using Convolutional Neural Networks Traffic Sign Recognition for Intelligent Vehicles Fraud Detection with Autoencoders Text Generation Using Recurrent Neural Networks Sentiment Analysis with Word EmbeddingsReviewsAuthor InformationMark 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. 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. Yuxi (Hayden) Liu is an experienced data scientist who's focused on developing machine learning and deep learning models and systems. He has worked in a variety of data-driven domains and has applied his machine learning expertise to computational advertising, recommendation, and network anomaly detection. He published five first-authored IEEE transaction and conference papers during his master's research at the University of Toronto. He is an education enthusiast and the author of a series of machine learning books. His first book, the first edition of Python Machine Learning By Example, was a #1 bestseller on Amazon India in 2017 and 2018. His other books include R Deep Learning Projects and Hands-On Deep Learning Architectures with Python published by Packt. Pablo Maldonado is an applied mathematician and data scientist with a taste for software development since his days of programming BASIC on a Tandy 1000. As an academic and business consultant, he spends a great deal of his time building applied artificial intelligence solutions for text analytics, sensor and transactional data, and reinforcement learning. Pablo earned his Ph.D. in applied mathematics (with focus on mathematical game theory) at the Universite Pierre et Marie Curie in Paris, France. Tab Content 6Author Website:Countries AvailableAll regions |
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