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Apress – Pro Deep Learning with TensorFlow 2017

Updated August 10, 2026 11.3 MB
Apress – Pro Deep Learning with TensorFlow 2017

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Description

Pro Deep Learning with TensorFlow is a specialized reference for a deep, mathematical understanding of advanced AI concepts using the popular TensorFlow library. Drawing on mathematical foundations such as linear algebra, statistics and probability, and optimization, the author helps the reader understand not only the coding but also the logic behind deep learning models.

The book content starts from the basics and moves to more complex architectures such as convolutional neural networks (CNN) for image processing and recurrent neural networks (RNN) for natural language processing. The updated editions also cover training in TensorFlow 2 and deploying complex models in operational and production environments.

Book Features

  • Comprehensive training in the mathematical and technical fundamentals required for deep learning.
  • Implementing various artificial intelligence architectures using Python and TensorFlow.
  • Review of unsupervised learning methods such as Autoencoders and RBMs.
  • Teaching advanced concepts such as Generative Aggregation Networks (GANs) and Graph Attention Networks.
  • Providing a complete guide to deploying artificial intelligence models in a real environment.
  • Using applied projects to transform theoretical concepts into smart applications.

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Pro Deep Learning with TensorFlow

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Pro Deep Learning with TensorFlow

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What is included

  • Comprehensive training in the mathematical and technical fundamentals required for deep learning.
  • Implementing various artificial intelligence architectures using Python and TensorFlow.
  • Review of unsupervised learning methods such as Autoencoders and RBMs.
  • Teaching advanced concepts such as Generative Aggregation Networks (GANs) and Graph Attention Networks.
  • Providing a complete guide to deploying artificial intelligence models in a real environment.
  • Using applied projects to transform theoretical concepts into smart applications.