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Full Description
The ability of a machine to learn how to create art makes for an intriguing partnership and perhaps a clear test of artificial intelligence. This book shows you how to create artificial intelligence capable of generating art-such as music and pictures-that can perform style transfers. You'll review the math behind generative ML models from classical Gaussian Mixture to the modern day GANs. Generative models are not something new in the ML world, but the recent creation of new types of Neural Networks such as GAN allows AI to produce something that can be truly called art. Rather than an academic and detached approach, this book offers concrete recipes with math explanations so that readers can learn by directly interacting with these models. Detailed code examples expand on each concept to create ML models on popular frameworks such as PyTorch and TensorFlow+Keras. The focus is on the practical aspects of music and picture generation with artificial intelligence-providing useful tips and best practices obtained from experience. Beyond GAN, different examples of how art can be generated with other types of models, even with simple statistical models, are presented. With AI for Art you'll learn about generative Machine Learning models on several levels and the math behind each model. What You'll LearnUnderstand the difference between generative and discriminative models, including Mixture models, Hidden Markov models, Bayesian networks, and more.Work with both PyTorch and TensorFlow+KerasCreate music, pictures, and text with AIWho This Book Is ForProgrammers with a knowledge of Python 3 and Deep Learning libraries, such as Keras and PyTorc. Libraries will be covered briefly for newer programmers less familiar with the topic.
Contents
1. Introduction and history of generative modelsProvide an historical overview and set some basics definitions2. Types of generative modelsProvide explanation for each type of model* Differences between generative and discriminative models* Mixture models* Hidden Markov model* Bayesian network * Latent Dirichlet allocation* Boltzmann machine * Autoencoders* Generative adversarial network3. Keras and PyTorch crash courceProvide an introduction for two popular deep learning libraries4. Music generationShowcase different examples of how music can be generated with ML* Statistical models* Recurrent Neural Networks and LSTM* Recurrent GAN5. Picture generation and style transferShowcase different examples of how pictures can be generated with ML* Convolutional Autoencoders* Convolutional GAN6. Text generationShowcase different examples of how text can be generated with ML* Markov models vs Kanye West* Seq-2-Seq and Chat Bots
