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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

Reshaping

Some operations, such as addition, can only be applied to tensors if they meet certain conditions. Reshaping is one method for modifying the shape of tensors so that such operations can be performed. Reshaping takes the elements of a tensor and rearranges them into a tensor of a different size. A tensor of any size can be reshaped so long as the number of total elements remains the same.

For example, a (4x3) matrix can be reshaped into a (6x2) matrix since they both have a total of 12 elements. The rank, or number, of dimensions, can also be changed in the reshaping process. For instance, a (4x3) matrix that has a rank equal to 2 can be reshaped into a (3x2x2) tensor that has a rank equal to 3. The (4x3) matrix can also be reshaped into a (12x1) vector in which the rank has changed from 2 to 1.

Figure 1.13 illustrates tensor reshaping. On the left is a tensor with shape (3x2), which can be reshaped to a tensor of shape equal to either (2x3), (6), or (6x1). Here...

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