As part of the best-selling Pocket Primer series, this book is designed to prepare programmersfor machine learning and deep learning/Tensor Flow topics. It begins with aquick introduction to Python, followed by chapters that discuss Num Py, Pandas, Matplotlib, and scikit-learn. The final two chapters contain an assortment of Tensor Flow 1.x code samples, including detailed code samples for Tensor Flow Dataset (which is used heavily in Tensor Flow 2 as well). A Tensor Flow 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 [email protected]:A practical introductionto Python, Num Py, Pandas, Matplotlib, and introductory aspects of Tensor Flow1.x Contains relevant Num Py/Pandascode samples that are typical in machine learning topics, and also useful Tensor Flow 1.x code samples for deep learning/Tensor Flow topics Includes many examples of Tensor Flow Dataset APIswith lazy operators, e.g., map(), filter(), batch(), take() and also methodchaining such operators Assumes the reader hasvery limited experience Companion files with all of thesource code examples (download from the publisher)
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