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)
