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Paperback
June 2019
9781683923640
More details
- Publisher
Mercury Learning and Information - Published
3rd June 2019 - ISBN 9781683923640
- Language English
- Pages 152 pp.
- Size 6" x 9"
$34.95
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May 2019
9781683923657
More details
- Publisher
Mercury Learning and Information - Published
9th May 2019 - ISBN 9781683923657
- Language English
- Pages 152 pp.
- Size 6" x 9"
$34.95
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May 2019
9781683923664
More details
- Publisher
Mercury Learning and Information - Published
9th May 2019 - ISBN 9781683923664
- Language English
- Pages 152 pp.
- Size 6" x 9"
$99.95
As part of the best-selling Pocket Primer series, this book is designed to introduce beginners to TensorFlow 1.x fundamentals for basic machine learning algorithms in TensorFlow. It is intended to be a fast-paced introduction to various “core” features of TensorFlow, with code samples that cover deep learning and TensorFlow basics. The material in the chapters illustrates how to solve a variety of tasks after which you can do further reading to deepen your knowledge. Companion files with all of the code samples are available for downloading from the publisher by writing to info@merclearning.com.
Features:
- Uses Python for code samples
- Covers TensorFlow APIs and Datasets
- Assumes the reader has very limited experience
- Companion files with all of the source code examples (download from the publisher)
"TensorFlow Pocket Primer introduces readers to TensorFlow 1x basics for machine learning algorithms, and is designed to be an introduction used either to supplement a course or for self-learning. It uses Python to cover code examples, assumes limited experience and background in the subject, and comes with supporting reference files containing all source code examples as a download from the publisher. From Cloud-based platforms to useful components of TensorFlow and their real-world applications, this primer will get anyone up and running in the shortest amount of time possible."
- Midwest Book Review
1: Introduction to TensorFlow
2: Useful TensorFlow APIs
3: TensorFlow Datasets
4: Linear Regression
5: Logistic Regression
On The Companion Files!
(available from the publisher for downloading by writing to info@merclearning.com)
- Source code samples
- Figures
Oswald Campesato
Oswald Campesato specializes in Deep Learning, Python, Data Science, and generative AI. He is the author/co-author of over forty-five books including Google Gemini for Python, Large Language Models, and GPT-4 for Developers (all Mercury Learning).