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. 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.

• Table of ContentsGetting Started with Deep LearningTraining a Prediction ModelDeep Learning FundamentalsTraining Deep Prediction ModelsImage Classification Using Convolutional Neural NetworksTuning and Optimizing ModelsNatural Language Processing Using Deep LearningDeep Learning Models Using TensorFlow in RAnomaly Detection and Recommendation SystemsRunning Deep Learning Models in the CloudThe Next Level in Deep Learning

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. 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.

• Table of ContentsGetting Started with Deep LearningTraining a Prediction ModelDeep Learning FundamentalsTraining Deep Prediction ModelsImage Classification Using Convolutional Neural NetworksTuning and Optimizing ModelsNatural Language Processing Using Deep LearningDeep Learning Models Using TensorFlow in RAnomaly Detection and Recommendation SystemsRunning Deep Learning Models in the CloudThe Next Level in Deep Learning

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. 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.

• Table of ContentsGetting Started with Deep LearningTraining a Prediction ModelDeep Learning FundamentalsTraining Deep Prediction ModelsImage Classification Using Convolutional Neural NetworksTuning and Optimizing ModelsNatural Language Processing Using Deep LearningDeep Learning Models Using TensorFlow in RAnomaly Detection and Recommendation SystemsRunning Deep Learning Models in the CloudThe Next Level in Deep Learning