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Learn TensorFlow Enterprise

You're reading from   Learn TensorFlow Enterprise Build, manage, and scale machine learning workloads seamlessly using Google's TensorFlow Enterprise

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Product type Paperback
Published in Nov 2020
Publisher Packt
ISBN-13 9781800209145
Length 314 pages
Edition 1st Edition
Languages
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Author (1):
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KC Tung KC Tung
Author Profile Icon KC Tung
KC Tung
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Table of Contents (15) Chapters Close

Preface 1. Section 1 – TensorFlow Enterprise Services and Features
2. Chapter 1: Overview of TensorFlow Enterprise FREE CHAPTER 3. Chapter 2: Running TensorFlow Enterprise in Google AI Platform 4. Section 2 – Data Preprocessing and Modeling
5. Chapter 3: Data Preparation and Manipulation Techniques 6. Chapter 4: Reusable Models and Scalable Data Pipelines 7. Section 3 – Scaling and Tuning ML Works
8. Chapter 5: Training at Scale 9. Chapter 6: Hyperparameter Tuning 10. Section 4 – Model Optimization and Deployment
11. Chapter 7: Model Optimization 12. Chapter 8: Best Practices for Model Training and Performance 13. Chapter 9: Serving a TensorFlow Model 14. Other Books You May Enjoy

Leveraging the TensorFlow Keras API

Keras is a deep learning API that wraps around machine learning libraries such as TensorFlow, Theano, and Microsoft Cognitive Toolkit (also known as CNTK). Its popularity as a standalone API stems from the succinct style of the model construction process. As of 2018, TensorFlow added Keras as a high-level API moving forward, and it is now known as tf.keras. Starting with the TensorFlow 2.0 distribution released in 2019, tf.keras has become the official high-level API.

tf.keras excels at modeling sophisticated deep learning architecture that contains long short-term memory (LSTM), gated recurring units (GRUs), and convolutional neural network (CNN) layers. These are considered to be workhorses in current natural language processing (NLP) and computer vision models. It also provides simple and straightforward architecture for simpler deep learning models, such as multilayer perceptrons. In the following example, we are going to use the tf.keras...

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