Python for TensorFlow Pocket Primer (Computing)

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 are available for downloading from the publisher by writing info@merclearning.com. Features: A practical introductionto Python, NumPy, Pandas, Matplotlib, and introductory aspects of TensorFlow1.x Contains relevant NumPy/Pandascode samples that are typical in machine learning topics, and also usefulTensorFlow 1.x code samples for deep learning/TensorFlow topics Includes many examples of TensorFlow 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) As part of the best-selling Pocket Primer series, this book is designed to prepare programmers for machine learning and deep learning/TensorFlow topics. It begins with a quick introduction to Python, followed by chapters that discuss NumPy, Pandas, Matplotlib, and scikit-learn. The final two chapters contain an assortment of TensorFlow 1.x code samples, including detailed code samples for TensorFlow Dataset (which is used heavily in TensorFlow 2 as well). A TensorFlow Dataset refers to the classes in the tf.data.Dataset namespace that enables programmers to construct a pipeline of data by means of method chaining so-called lazy operators, e.g., map(), filter(), batch(), and so forth, based on data from one or more data sources. Companion files with source code are available for downloading from the publisher by writing info@merclearning.com. Features: A practical introduction to Python, NumPy, Pandas, Matplotlib, and introductory aspects of TensorFlow 1.x Contains relevant NumPy/Pandas code samples that are typical in machine learning topics, and also useful TensorFlow 1.x code samples for deep learning/TensorFlow topics Includes many examples of TensorFlow Dataset APIs with lazy operators, e.g., map(), filter(), batch(), take() and also method chaining such operators Assumes the reader has very limited experience Companion files with all of the source code examples (download from the publisher) Prepares programmers for machine learning and deep learning/TensorFlow topics. The book begins with a quick introduction to Python, followed by chapters that discuss NumPy, Pandas, Matplotlib, and scikit-learn. The final two chapters contain an assortment of TensorFlow 1.x code samples, including detailed code samples for TensorFlowDataset. Intended for software developers who are advanced beginners, this book is designed to prepare programmers for machine learning and deep learning/TensorFlow topics. -- Edited summary from book