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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

The Convolutional Layer

Think of a convolution as nothing more than an image transformer with three key elements. First, there is an input image, then a filter, and finally, a feature map.

This section will cover each of these in turn to give you a solid idea of how images are filtered in a convolutional layer. The convolution is the process of passing a filter window over the input data, which will result in a map of activations known as a feature map. The input data may be the input image to the model or the output of a prior, intermediary layer of the model. The filter is generally a much smaller array, such as 3x3 for two-dimensional data, in which the specific values of the filter are learned during the training process. The filter passes across the input data with a window size equal to the size of the filter, then, the scalar product of the filter and section of the input data is applied, producing what's known as an activation. As this process continues across the entire...

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