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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

Multi-Class Classification

With binary classification, you were limited to dealing with target variables that can only take two possible values: 0 and 1 (false or true). Multi-class classification can be seen as an extension of this and allows the target variable to have more than two values (or you can say binary classification is just a subset of multi-class classification). For instance, a model that predicts different levels of disease severity for a patient or another one that classifies users into different groups based on their past shopping behaviors will be multi-class classifiers.

In the next section, you will dive into the softmax function, which is used for multi-class classification.

The Softmax Function

Binary classifiers require a specific activation function for the last fully connected layer of a neural network, which is sigmoid. The activation function specific to multi-class classifiers is different. It is softmax. Its formula is as follows:

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The TensorFlow Workshop
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