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Deep Learning with Keras

Deep Learning with Keras  #AI #DeepLearning #books

  • Implement various deep-learning algorithms in Keras and see how deep-learning can be used in games See how various deep-learning models and practical use-cases can be implemented using Keras A practical, hands-on guide with real-world examples to give you a strong foundation in Keras

    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.

  • 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.

Implement various deep-learning algorithms in Keras and see how deep-learning can be used in games
See how various deep-learning models and practical use-cases can be implemented using Keras
A practical, hands-on guide with real-world examples to give you a strong foundation in Keras

@cvo_website: Deep Learning with Keras #AI #DeepLearning #books

Implement various deep-learning algorithms in Keras and see how deep-learning can be used in games See how various deep-learning models and practical use-cases can be implemented using Keras A practical, hands-on guide with real-world examples to give you a strong foundation in Keras

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.

Deep Learning with Keras

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