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 [email protected].

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)