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The Deep Learning with Keras Workshop

You're reading from   The Deep Learning with Keras Workshop Learn how to define and train neural network models with just a few lines of code

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Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781800562967
Length 496 pages
Edition 1st Edition
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Authors (3):
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Matthew Moocarme Matthew Moocarme
Author Profile Icon Matthew Moocarme
Matthew Moocarme
Mahla Abdolahnejad Mahla Abdolahnejad
Author Profile Icon Mahla Abdolahnejad
Mahla Abdolahnejad
Ritesh Bhagwat Ritesh Bhagwat
Author Profile Icon Ritesh Bhagwat
Ritesh Bhagwat
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Table of Contents (11) Chapters Close

Preface
1. Introduction to Machine Learning with Keras 2. Machine Learning versus Deep Learning FREE CHAPTER 3. Deep Learning with Keras 4. Evaluating Your Model with Cross-Validation Using Keras Wrappers 5. Improving Model Accuracy 6. Model Evaluation 7. Computer Vision with Convolutional Neural Networks 8. Transfer Learning and Pre-Trained Models 9. Sequential Modeling with Recurrent Neural Networks Appendix

Recurrent Neural Networks (RNNs)

RNNs are a class of neural networks that are built on the concept of sequential memory. Unlike traditional neural networks, an RNN predicts the results in sequential data. Currently, an RNN is the most robust technique that's available for processing sequential data.

If you have access to a smartphone that has Google Assistant, try opening it and asking the question: "When was the United Nations formed?" The answer is displayed in the following screenshot:

Figure 9.2: Google Assistant's output

Now, ask a second question, "Why was it formed?", as follows:

Figure 9.3: Google Assistant's contextual output

Now, ask the third question, "Where are its headquarters?", and you should get the following answer:

Figure 9.4: Google Assistant's output

One interesting thing to note here is that we only mentioned the "United Nations...

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