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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 classification models can solve problems when the response variable is discrete. You also saw different metrics used to assess the performance of such classifiers. You got hands-on experience in building and training binary, multi-class, and multi-label classifiers with TensorFlow.

When evaluating a model, you will face three different situations: model overfitting, model underfitting, and model performing. The last one is the ideal scenario, in which a model is accurately predicting the right outcome and is generalizing to unseen data well.

If a model is underfitting, it means it is neither achieving satisfactory performance nor accurately predicting the target variable. In this case, a data scientist can try tuning different hyperparameters and finding the best combination that will boost the accuracy of the model. Another possibility is to improve the input dataset by handling issues such as the cleanliness of the...

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