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].
Features:
- 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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