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The TensorFlow Workshop

You're reading from   The TensorFlow Workshop A hands-on guide to building deep learning models from scratch using real-world datasets

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Product type Paperback
Published in Dec 2021
Publisher Packt
ISBN-13 9781800205253
Length 600 pages
Edition 1st Edition
Languages
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Authors (4):
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Matthew Moocarme Matthew Moocarme
Author Profile Icon Matthew Moocarme
Matthew Moocarme
Abhranshu Bagchi Abhranshu Bagchi
Author Profile Icon Abhranshu Bagchi
Abhranshu Bagchi
Anthony Maddalone Anthony Maddalone
Author Profile Icon Anthony Maddalone
Anthony Maddalone
Anthony So Anthony So
Author Profile Icon Anthony So
Anthony So
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Toc

Table of Contents (13) Chapters Close

Preface
1. Introduction to Machine Learning with TensorFlow 2. Loading and Processing Data FREE CHAPTER 3. TensorFlow Development 4. Regression and Classification Models 5. Classification Models 6. Regularization and Hyperparameter Tuning 7. Convolutional Neural Networks 8. Pre-Trained Networks 9. Recurrent Neural Networks 10. Custom TensorFlow Components 11. Generative Models Appendix

Summary

In this chapter, you used a variety of TensorFlow resources, including TensorBoard, TensorFlow Hub, and Google Colab. TensorBoard offers users a method to visualize computational model graphs, metrics, and any experimentation results. TensorFlow Hub allows users to accelerate their machine learning development using pre-trained models built by experts in the field. Google Colab provides a collaborative environment to develop machine learning models on Google servers. Developing performant machine learning models is an iterative process of trial and error, and the ability to visualize every step of the process can help practitioners debug and improve their models. Moreover, understanding how experts in the field have built their models and being able to utilize the pre-learned weights in the networks can drastically reduce training time. All of these resources are used to provide an environment to develop and debug machine learning algorithms in an efficient workflow.

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