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Deep learning with Keras : Implementing deep learning models and neural networks with the power of Python by Antonio Gulli; Sujit Pal

By: Material type: TextTextPublication details: Birmingham-Mumbai: Packt, 2017.Edition: 1 EdDescription: i-iv+304pISBN:
  • 9781787128422
Subject(s): DDC classification:
  • 005.133 GUL-D
Contents:
Neural networks foundations -- Keras installation and API -- Deep learning with ConvNets -- Generative adversarial networks and wavenet -- World embeddings -- Recurrent neural networks - RNN -- Additional deep learning models -- AI game playing -- Conclusion.
Summary: 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 hand written 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 (GAN). 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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Item type Current library Home library Call number Status Date due Barcode
Book Book Dept. of Computational Biology and Bioinformatics Processing Center Dept. of Computational Biology and Bioinformatics 005.133 GUL-D (Browse shelf(Opens below)) Available DCB3873

Neural networks foundations --
Keras installation and API --
Deep learning with ConvNets --
Generative adversarial networks and wavenet --
World embeddings --
Recurrent neural networks - RNN --
Additional deep learning models --
AI game playing --
Conclusion.


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 hand written 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 (GAN). 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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