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Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play. David Foster

Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play


Generative-Deep-Learning.pdf
ISBN: 9781492041948 | 322 pages | 9 Mb

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  • Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play
  • David Foster
  • Page: 322
  • Format: pdf, ePub, fb2, mobi
  • ISBN: 9781492041948
  • Publisher: O'Reilly Media, Incorporated
Download Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play


Google free audio books download Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play by David Foster

Generative modeling is one of the hottest topics in artificial intelligence. Recent advances in the field have shown how it’s possible to teach a machine to excel at human endeavors—such as drawing, composing music, and completing tasks—by generating an understanding of how its actions affect its environment. With this practical book, machine learning engineers and data scientists will learn how to recreate some of the most famous examples of generative deep learning models, such as variational autoencoders and generative adversarial networks (GANs). You’ll also learn how to apply the techniques to your own datasets. David Foster, cofounder of Applied Data Science, demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to the most cutting-edge algorithms in the field. Through tips and tricks, you’ll learn how to make your models learn more efficiently and become more creative. Get a fundamental overview of generative modeling Learn how to use the Keras and TensorFlow libraries for deep learning Discover how variational autoencoders (VAEs) work Get practical examples of generative adversarial networks (GANs) Understand how to build generative models that learn how to paint, write, and compose Apply generative models within a reinforcement learning setting to accomplish tasks

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Generative modeling is an unsupervised learning task in machine learning . are adversarial in the game theory sense, and are playing a zero-sum game. in turn teaching the generator to generate examples of that class in order to Creating Art. The ability to great new and artistic images, sketches, painting, and more. Deep generative neural networks for novelty generation - Theses.fr
8.2 Machine learning and novelty generation: the innovation engine, in situations where the task is hard to write down formally and thus an explicit al- to a painting, or play music by mimicking sounds of acoustic instruments. The because the decoder is constrained to compose the stroke features in such a way. The GANfather: The man who's given machines the gift of imagination
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