In this chapter, we covered some deep learning networks that were not covered in earlier chapters. We started with a brief look into the Keras functional API, which allows us to build networks that are more complex than the sequential networks we have seen so far. We then looked at regression networks, which allow us to do predictions in a continuous space, and opens up a whole new range of problems we can solve. However, a regression network is really a very simple modification of a standard classification network. The next area we looked at was autoencoders, which are a style of network that allows us to do unsupervised learning and make use of the massive amount of unlabeled data that all of us have access to nowadays. We also learned how to compose the networks we had already learned about as giant Lego-like building blocks into larger and more interesting networks. We then moved from building large networks using smaller networks, to learning how to customize individual layers...
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.
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Current Title:
Deep Learning with Keras
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