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Python: Advanced Guide to Artificial Intelligence

You're reading from   Python: Advanced Guide to Artificial Intelligence Expert machine learning systems and intelligent agents using Python

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Product type Course
Published in Dec 2018
Publisher Packt
ISBN-13 9781789957211
Length 764 pages
Edition 1st Edition
Languages
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Authors (2):
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Giuseppe Bonaccorso Giuseppe Bonaccorso
Author Profile Icon Giuseppe Bonaccorso
Giuseppe Bonaccorso
Rajalingappaa Shanmugamani Rajalingappaa Shanmugamani
Author Profile Icon Rajalingappaa Shanmugamani
Rajalingappaa Shanmugamani
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Table of Contents (31) Chapters Close

Title Page
About Packt
Contributors
Preface
1. Machine Learning Model Fundamentals FREE CHAPTER 2. Introduction to Semi-Supervised Learning 3. Graph-Based Semi-Supervised Learning 4. Bayesian Networks and Hidden Markov Models 5. EM Algorithm and Applications 6. Hebbian Learning and Self-Organizing Maps 7. Clustering Algorithms 8. Advanced Neural Models 9. Classical Machine Learning with TensorFlow 10. Neural Networks and MLP with TensorFlow and Keras 11. RNN with TensorFlow and Keras 12. CNN with TensorFlow and Keras 13. Autoencoder with TensorFlow and Keras 14. TensorFlow Models in Production with TF Serving 15. Deep Reinforcement Learning 16. Generative Adversarial Networks 17. Distributed Models with TensorFlow Clusters 18. Debugging TensorFlow Models 19. Tensor Processing Units
20. Getting Started 21. Image Classification 22. Image Retrieval 23. Object Detection 24. Semantic Segmentation 25. Similarity Learning 1. Other Books You May Enjoy Index

Application areas of RNNs


Some of the application areas where RNNs are used more often are as follows:

  • Natural Language Modeling: The RNN models have been used in natural language processing (NLP) for natural language understanding and natural language generation tasks. In NLP, an RNN model is given a sequence of words and it predicts another sequence of words. Thus, the trained models can be used for generating the sequence of words, a field known as Text Generation. For example, generating stories, and screenplays. Another area of NLP is language translation, where given a sequence of words in one language, the model predicts a sequence of words in another language.
  • Voice and Speech Recognition: The RNN models have great use in building models for learning from the audio data. In speech recognition, an RNN model is given audio data and it predicts a sequence of phonetic segments. It can be used to train the models to recognize the voice commands, or even for conversation with speech based...
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