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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

Optimization

In this section, you will learn about some optimization approaches that are fundamental to training machine learning models. Optimization is the process by which the weights of the layers of an ANN are updated such that the error between the predicted values of the ANN and the true values of the training data is minimized.

Forward Propagation

Forward propagation is the process by which information propagates through ANNs. Operations such as a series of tensor multiplications and additions occur at each layer of the network until the final output. Forward propagation is explained in Figure 1.37, showing a single hidden layer ANN. The input data has two features, while the output layer has a single value for each input record.

The weights and biases for the hidden layer and output are shown as matrices and vectors with the appropriate indexes. For the hidden layer, the number of rows in the weight matrix is equal to the number of features of the input, and...

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