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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

Back Propagation Through Time (BPTT)

There are many types of sequential models. You've already used simple RNNs, deep RNNs, and LSTMs. Let's take a look at a couple of additional models used for NLP.

Remember that you trained feed-forward models by first making a forward pass through the network that goes from input to output. This is the standard feed-forward model where the layers are densely connected. To train this kind of model, you can backpropagate the gradients through the network, taking the derivative of the loss of each weight parameter in the network. Then, you can adjust the parameters to minimize the loss.

But in RNNs, as discussed earlier, your forward pass through the network also consists of going forward in time, updating the cell state based on the input and the previous state, and generating an output, Y. At that time step, computing a loss and then finally summing these losses from the individual time steps gets your total loss.

This means that...

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