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. 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, Includes companion files with all of
the source code examples (download from the publisher).
General
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