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Can I read Deep learning with Keras : implementing deep learning models and neural networks with the power of Python on EtoBox?
Deep learning with Keras : implementing deep learning models and neural networks with the power of Python by Antonio Gulli, Sujit Pal is a science book available to read on EtoBox.
What is Deep learning with Keras : implementing deep learning models and neural networks with the power of Python about?
Get to grips with the basics of Keras to implement fast and efficient deep-learning models Key Features • 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 Book Description 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
Who reads Deep learning with Keras : implementing deep learning models and neural networks with the power of Python?
It is typically read by self-directed learners exploring a subject in depth.
Common subject areas: history, science, philosophy, social sciences.
- Author
- Antonio Gulli, Sujit Pal
- Publisher
- Packt; Packt Publishing
- Published
- 2017
- Language
- EN
- ISBN
- 9781787128422
- Category
- science
- Subjects
- Science, Computer Science, Stem
- Rating
- 3.8 / 5 (71 ratings)
- Updated
- 2026-03-14
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