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  • Book Overview & Buying The TensorFlow Workshop
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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

Sequential Data

Sequential data is information that happens in a sequence and is related to past and future data. An example of sequential data is time series data; as you perceive it, time only travels in one direction.

Suppose you have a ball (as in Figure 9.2), and you want to predict where this ball will travel next. If you have no prior information about the direction from which the ball was thrown, you will simply have to guess. However, if in addition to the ball's current location, you also had information about its previous location, the problem would be much simpler. To be able to predict the ball's next location, you need the previous location information in a sequential (or ordered) form to make a prediction about future events.

Figure 9.2: Direction of the ball

RNNs function in a way that allows the sequence of the information to retain value with the help of internal memory.

You'll take a look at some examples of sequential...

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The TensorFlow Workshop
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