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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

Image Representation

First, consider how a computer processes an image. To a computer, images are numbers. To be able to work with images for classification or object identification, you need to understand how a model transforms an image input into data. A pixel in an image file is just a piece of data.

In the following figure, you can see an example of pixel values for a grayscale image of the number eight. For the 28x28-pixel image, there are a total of 784 pixels. Each pixel has a value between 0 and 255 identifying how light or dark the pixel is. On the right side, there is one large column vector with each pixel value listed. This is used by the model to identify the image.

Figure 7.2: Pixel values

Now that you know what the input data looks like, it's time to get a closer look at the convolutional process—more specifically, the convolutional layer.

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