Description As part of the best-selling Pocket Primer series, this book is designed to prepare programmers for machine learning and deep learning/Tensor Flow topics. 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). x code samples for deep learning/Tensor Flow topics Includes many examples of Tensor Flow Dataset APIs with lazy operators, e. xContains relevant Num Py/Pandas code samples that are typical in machine learning topics, and also useful Tensor Flow 1.
Features: A practical introduction to Python, Num Py, Pandas, Matplotlib, and introductory aspects of Tensor Flow 1. com.
Companion files with source code are available for downloading from the publisher by writing info@merclearning. g., map(), filter(), batch(), and so forth, based on data from one or more data sources.
Dataset namespace that enables programmers to construct a pipeline of data by means of method chaining so-called lazy operators, e. data.
A Tensor Flow Dataset refers to the classes in the tf. x code samples, including detailed code samples for Tensor Flow Dataset (which is used heavily in Tensor Flow 2 as well).
The final two chapters contain an assortment of Tensor Flow 1.
It begins with a quick introduction to Python, followed by chapters that discuss Num Py, Pandas, Matplotlib, and scikit-learn.
Description As part of the best-selling Pocket Primer series, this book is designed to prepare programmers for machine learning and deep learning/Tensor Flow topics
