Python for TensorFlow Pocket Primer (eBook, PDF)
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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 met...
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 info@merclearning.com.
Features:
Companion files with source code areavailable 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)
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