Python for TensorFlow Pocket Primer

As part of the best-selling PocketPrimer series, this book is designed to prepare programmersfor machine learning and deep learning/TensorFlow topics. It begins with aquick introduction to Python, followed by chapters that discuss NumPy, Pandas,Matplotlib, and scikit-learn. The final two chapters contain an assortment ofTensorFlow 1.x code samples, including detailed code samples for TensorFlowDataset (which is used heavily in TensorFlow 2 as well). A TensorFlow Datasetrefers to the classes in the tf.data.Dataset namespace that enables programmersto construct a pipeline of data by means of method chaining so-called lazyoperators, e.g., map(), filter(), batch(), and so forth, based on data from oneor more data sources.Companion files with source code areavailable for downloading from the publisher by writing info@merclearning.com.Features:A practical introductionto Python, NumPy, Pandas, Matplotlib, and introductory aspects of TensorFlow1.xContains relevant NumPy/Pandascode samples that are typical in machine learning topics, and also usefulTensorFlow 1.x code samples for deep learning/TensorFlow topicsIncludes many examples of TensorFlow Dataset APIswith lazy operators, e.g., map(), filter(), batch(), take() and also methodchaining such operatorsAssumes the reader hasvery limited experienceCompanion files with all of thesource code examples (download from the publisher)

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Oswald Campesato (San Francisco, CA) specializes in Data Cleaning,Java, Android, and CSS3/SVG graphics. He is the author/co-author of over twenty-fivebooks including Android Pocket Primer,Angular4Pocket Primer, and thePython Pocket Primer (MercuryLearning).