The key intuition of GAN can be easily considered as analogous to art forgery, which is the process of creating works of art (https://en.wikipedia.org/wiki/Art) that are falsely credited to other, usually more famous, artists. GANs train two neural nets simultaneously, as shown in the next diagram. The generator G(Z) makes the forgery, and the discriminator D(Y) can judge how realistic the reproductions based on its observations of authentic pieces of arts and copies are. D(Y) takes an input, Y, (for instance, an image) and expresses a vote to judge how real the input is--in general, a value close to zero denotes real and a value close to one denotes forgery. G(Z) takes an input from a random noise, Z, and trains itself to fool D into thinking that whatever G(Z) produces is real. So, the goal of training the discriminator D(Y) is to maximize D(Y) for...
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Deep Learning with Keras
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Deep Learning with Keras
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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 (10 chapters)
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
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