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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

Introduction

In the previous chapter, you learned how to use some TensorFlow resources to aid in development. These included TensorBoard (for visualizing computational graphs), TensorFlow Hub (an online repository for machine learning modules), and Google Colab (an online Python development environment for running code on Google servers). All these resources help machine learning practitioners develop models efficiently.

In this chapter, you will explore how to create ANNs using TensorFlow. You will build ANNs with different architectures to solve regression and classification tasks. Regression tasks aim to predict continuous variables from the input training data, while classification tasks aim to classify the input data into two or more classes. For example, a model to predict whether or not it will rain on a given day is a classification task since the result of the model will be of two classes—rain or no rain. However, a model to predict the amount of rain on a given...

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