this book was written for anyone who wishes to explore deep learning from scratch or broaden their understanding of deep learning whether you're practicing machine-learning engineera software developeror college studentyou'll find value in these pages this book offers practicalhands-on exploration of deep learning it avoids mathematical notationpreferring instead to explain quantitative concepts via code snippets and to build practical intuition about the core ideas of machine learning and deep learning you'll learn from more than code examples that include detailed commentarypractical recommendationsand simple high-level explanations of everything you need to know to start using deep learning to solve concrete problems the code examples use the python deep-learning framework keraswith tensorflow as backend engine kerasone of the most popular and fastest-growing deeplearning frameworksis widely recommended as the best tool to get started with deep learning after reading this bookyou'll have solid understand of what deep learning iswhen it' applicableand what its limitations are you'll be familiar with the standard workflow for approaching and solving machine-learning problemsand you'll know how to address commonly encountered issues you'll be able to use keras to tackle real-world problems ranging from computer vision to natural-language processingimage classificationtimeseries forecastingsentiment analysisimage and text generationand more xvi licensed to