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Paperback
September 2019
9781683924609
More details
- Publisher
Mercury Learning and Information - Published
18th September 2019 - ISBN 9781683924609
- Language English
- Pages 252 pp.
- Size 6" x 9"
$35.95
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August 2019
9781683924593
More details
- Publisher
Mercury Learning and Information - Published
27th August 2019 - ISBN 9781683924593
- Language English
- Pages 252 pp.
- Size 6" x 9"
$35.95
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August 2019
9781683924616
More details
- Publisher
Mercury Learning and Information - Published
27th August 2019 - ISBN 9781683924616
- Language English
- Pages 252 pp.
- Size 6" x 9"
$99.95
As part of the best-selling Pocket Primer series, this book is designed to introduce beginners to basic machine learning algorithms using TensorFlow 2. It is intended to be a fast-paced introduction to various “core” features of TensorFlow, with code samples that cover machine learning and TensorFlow basics. A comprehensive appendix contains some Keras-based code samples and the underpinnings of MLPs, CNNs, RNNs, and LSTMs. 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 emailing proof of purchase to info@merclearning.com.
Features:
- Uses Python for code samples
- Covers TensorFlow 2 APIs and Datasets
- Includes a comprehensive appendix that covers Keras and advanced topics such as NLPs, MLPs, RNNs, LSTMs
- Features the companion files with all of the source code examples and figures (download from the publisher)
1: Introduction to TensorFlow 2
2: Useful TensorFlow 2 APIs
3: TensorFlow 2 Datasets
4: Linear Regression
5: Working with Classifiers
Appendix: TF2, Keras, and Advanced Topics
Index
On the Companion Files:
(available from the publisher for downloading)
- Source code samples from the text
- 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).