Advanced Deep Learning with Python

Advanced Deep Learning with Python

eBook Details:

  • Paperback: 468 pages
  • Publisher: WOW! eBook (December 12, 2019)
  • Language: English
  • ISBN-10: 178995617X
  • ISBN-13: 978-1789956177

eBook Description:

Advanced Deep Learning with Python: Cover modern advanced deep learning areas like convolutional networks, recurrent networks, attention mechanism, meta learning, graph neural networks, memory augmented neural networks, and more using the Python ecosystem

In order to build robust deep learning systems, you’ll need to understand everything from how neural networks work to training CNN models. In this book, you’ll discover newly developed deep learning models, methodologies used in the domain, and their implementation based on areas of application.

You’ll start by understanding the building blocks and the math behind neural networks, and then move on to CNNs and their advanced applications in computer vision. You’ll also learn to apply the most popular CNN architectures in object detection and image segmentation. Further on, you’ll focus on variational autoencoders and GANs. You’ll then use neural networks to extract sophisticated vector representations of words, before going on to cover various types of recurrent networks, such as LSTM and GRU. You’ll even explore the attention mechanism to process sequential data without the help of recurrent neural networks (RNNs). Later, you’ll use graph neural networks for processing structured data, along with covering meta-learning, which allows you to train neural networks with fewer training samples. Finally, you’ll understand how to apply deep learning to autonomous vehicles.

  • Cover advanced and state-of-the-art neural network architectures
  • Understand the theory and math behind neural networks
  • Train DNNs and apply them to modern deep learning problems
  • Use CNNs for object detection and image segmentation
  • Implement generative adversarial networks (GANs) and variational autoencoders to generate new images
  • Solve natural language processing (NLP) tasks, such as machine translation, using sequence-to-sequence models
  • Understand DL techniques, such as meta-learning and graph neural networks

By the end of this Advanced Deep Learning with Python book, you’ll have mastered key deep learning concepts and the different applications of deep learning models in the real world.

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1 Response

  1. April 19, 2020

    […] Deep Learning Pipeline: Building a Deep Learning Model with TensorFlow […]

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