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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

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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