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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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Toc

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

Chapter 8. Advanced Neural Models

In this chapter, we will explore pragmatic world of deep learning, analyzing two very important elements: deep convolutional networks and recurrent neural networks (RNN). The former represents the most accurate and best performing visual processing technique for almost any purpose. Results like the ones obtained in fields such as real-time image recognition, self-driving cars, and Deep Reinforcement Learning have been possible thanks to the expressivity of this kind of network. On the other hand, in order to fully manage the temporal dimension, it is necessary to introduce advanced recurrent layers, whose performance must be greater than any other regression method. Employing these two techniques together with all the elements already discussed in the previous chapter makes it possible to achieve extraordinary results in the field of video processing, decoding, segmentation, and generation.

In particular, in this chapter, we are going to discuss the following...

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