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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

Generative Adversarial Networks

GANs are networks that generate new, synthetic data by learning patterns and underlying representations from a training dataset. The GAN does this by using two networks that compete with one another in an adversarial fashion. These networks are called the generator and discriminator.

To see how these networks compete with one another, consider the following example. The example will skip over a few details that will make more sense as you get to them later in the chapter.

Imagine two entities: a money counterfeiter and a business owner. The counterfeiter attempts to make a currency that looks authentic to fool the business owner into thinking the currency is legitimate. By contrast, the business owner tries to identify any fake bills, so that they don't end up with just a piece of worthless paper instead of real currency.

This is essentially what GANs do. The counterfeiter in this example is the generator, and the business owner is...

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