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Python Machine Learning

You're reading from   Python Machine Learning Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2

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Product type Paperback
Published in Dec 2019
Publisher Packt
ISBN-13 9781789955750
Length 772 pages
Edition 3rd Edition
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Authors (2):
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Vahid Mirjalili Vahid Mirjalili
Author Profile Icon Vahid Mirjalili
Vahid Mirjalili
Sebastian Raschka Sebastian Raschka
Author Profile Icon Sebastian Raschka
Sebastian Raschka
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Table of Contents (21) Chapters Close

Preface 1. Giving Computers the Ability to Learn from Data 2. Training Simple Machine Learning Algorithms for Classification FREE CHAPTER 3. A Tour of Machine Learning Classifiers Using scikit-learn 4. Building Good Training Datasets – Data Preprocessing 5. Compressing Data via Dimensionality Reduction 6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning 7. Combining Different Models for Ensemble Learning 8. Applying Machine Learning to Sentiment Analysis 9. Embedding a Machine Learning Model into a Web Application 10. Predicting Continuous Target Variables with Regression Analysis 11. Working with Unlabeled Data – Clustering Analysis 12. Implementing a Multilayer Artificial Neural Network from Scratch 13. Parallelizing Neural Network Training with TensorFlow 14. Going Deeper – The Mechanics of TensorFlow 15. Classifying Images with Deep Convolutional Neural Networks 16. Modeling Sequential Data Using Recurrent Neural Networks 17. Generative Adversarial Networks for Synthesizing New Data 18. Reinforcement Learning for Decision Making in Complex Environments 19. Other Books You May Enjoy 20. Index

Other GAN applications

In this chapter, we mainly focused on generating examples using GANs and looked at a few tricks and techniques to improve the quality of synthesized outputs. The applications of GANs are expanding rapidly, including in computer vision, machine learning, and even other domains of science and engineering. A nice list of different GAN models and application areas can be found at https://github.com/hindupuravinash/the-gan-zoo.

It is worth mentioning that we covered GANs in an unsupervised fashion, that is, no class label information was used in the models that were covered in this chapter. However, the GAN approach can be generalized to semi-supervised and supervised tasks, as well. For example, the conditional GAN (cGAN) proposed by Mehdi Mirza and Simon Osindero in the paper Conditional Generative Adversarial Nets (https://arxiv.org/pdf/1411.1784.pdf) uses the class label information and learns to synthesize new images conditioned on the provided label, that...

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