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The TensorFlow Workshop

The TensorFlow Workshop

By : Matthew Moocarme, Abhranshu Bagchi, Anthony So , Maddalone
4.6 (25)
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The TensorFlow Workshop

The TensorFlow Workshop

4.6 (25)
By: Matthew Moocarme, Abhranshu Bagchi, Anthony So , Maddalone

Overview of this book

Getting to grips with tensors, deep learning, and neural networks can be intimidating and confusing for anyone, no matter their experience level. The breadth of information out there, often written at a very high level and aimed at advanced practitioners, can make getting started even more challenging. If this sounds familiar to you, The TensorFlow Workshop is here to help. Combining clear explanations, realistic examples, and plenty of hands-on practice, it’ll quickly get you up and running. You’ll start off with the basics – learning how to load data into TensorFlow, perform tensor operations, and utilize common optimizers and activation functions. As you progress, you’ll experiment with different TensorFlow development tools, including TensorBoard, TensorFlow Hub, and Google Colab, before moving on to solve regression and classification problems with sequential models. Building on this solid foundation, you’ll learn how to tune models and work with different types of neural network, getting hands-on with real-world deep learning applications such as text encoding, temperature forecasting, image augmentation, and audio processing. By the end of this deep learning book, you’ll have the skills, knowledge, and confidence to tackle your own ambitious deep learning projects with TensorFlow.
Table of Contents (13 chapters)
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Preface

Introduction

In the previous chapter, you learned how to load and process a variety of data types so that they can be used in TensorFlow modeling. This included tabular data from CSV files, image data, text data, and audio files. By the end of the chapter, you were able to process all these data types and produce numerical tensors from them that can be input for model training.

In this chapter, you will learn about TensorFlow resources that will aid you in your model building and help you create performant machine learning algorithms. You will explore the practical resources that practitioners can utilize to aid their development workflow, including TensorBoard, TensorFlow Hub, and Google Colab. TensorBoard is an interactive platform that offers a visual representation of the computational graphs and data produced during the TensorFlow development process. The platform solves the problem of visualizing various data types that is common in machine learning. The visualization toolkit...

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