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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

TensorBoard

TensorBoard is a visualization toolkit used to aid in machine learning experimentation. The platform has dashboard functionality for visualizing many of the common data types that a data science or machine learning practitioner may need at once, such as scalar values, image batches, and audio files. While such visualizations can be created with other plotting libraries, such as matplotlib or ggplot, TensorBoard combines many visualizations in an easy-to-use environment. Moreover, all that is required to create the visualizations is to log the trace during the building, fitting, and evaluating steps. TensorBoard helps in the following tasks:

  • Visualizing the model graph to view and understand the model's architecture:

Figure 3.1: A visual representation of model graphs and functions in TensorBoard

  • Viewing histograms and distributions of variables and tracking how they change over time.
  • Displaying images, text, and audio data. For example, the...

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