In this chapter, we discussed GANs. A GAN typically consists of two networks; one is trained to forge synthetic data that looks authentic, and the second is trained to discriminate authentic data against forged data. The two networks continuously compete, and in doing so, they keep improving each other. We reviewed an open source code, learning to forge MNIST and CIFAR-10 images that look authentic. In addition, we discussed WaveNet, a deep generative network proposed by Google DeepMind for teaching computers how to reproduce human voices and musical instruments with impressive quality. WaveNet directly generates raw audio with a parametric text-to-speech approach based on dilated convolutional networks. Dilated convolutional networks are a special kind of ConvNets where convolution filters have holes, allowing the receptive field to grow exponentially in depth and therefore efficiently cover thousands of audio time-steps. DeepMind showed how it is possible to use WaveNet to synthesize...
Deep Learning with Keras
By :
Deep Learning with Keras
By:
Overview of this book
This book starts by introducing you to supervised learning algorithms such as simple linear regression, the classical multilayer perceptron and more sophisticated deep convolutional networks. You will also explore image processing with recognition of handwritten digit images, classification of images into different categories, and advanced objects recognition with related image annotations. An example of identification of salient points for face detection is also provided.
Next you will be introduced to Recurrent Networks, which are optimized for processing sequence data such as text, audio or time series. Following that, you will learn about unsupervised learning algorithms such as Autoencoders and the very popular Generative Adversarial Networks (GANs). You will also explore non-traditional uses of neural networks as Style Transfer.
Finally, you will look at reinforcement learning and its application to AI game playing, another popular direction of research and application of neural networks.
Table of Contents (16 chapters)
Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Free Chapter
Neural Networks Foundations
Keras Installation and API
Deep Learning with ConvNets
Generative Adversarial Networks and WaveNet
Word Embeddings
Recurrent Neural Network — RNN
Additional Deep Learning Models
AI Game Playing
Conclusion
Customer Reviews