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Deep Learning with Python

Amazon.com Price:  $47.49 (as of 22/04/2019 22:23 PST- Details)

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Description

Summary

Deep Learning with Python introduces the field of deep learning the use of the Python language and the powerful Keras library. Written by Keras author and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the Technology

Machine learning has made remarkable progress in latest years. We went from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn’t beat a serious Go player, to defeating a world champion. At the back of this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of prior to now not possible smart applications.

About the Book

Deep Learning with Python introduces the field of deep learning the use of the Python language and the powerful Keras library. Written by Keras author and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You’ll be able to explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the point you finish, You’ll be able to have the knowledge and hands-on skills to apply deep learning in your own projects.

What’s Inside

  • Deep learning from first principles
  • Setting up your own deep-learning environment
  • Image-classification models
  • Deep learning for text and sequences
  • Neural style transfer, text generation, and image generation

About the Reader

Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required.

About the Author

François Chollet works on deep learning at Google in Mountain View, CA. He’s the author of the Keras deep-learning library, in addition to a contributor to the TensorFlow machine-learning framework. He also does deep-learning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others.

Table of Contents

    PART 1 – FUNDAMENTALS OF DEEP LEARNING

  1. What is deep learning?
  2. Before we begin: the mathematical building blocks of neural networks
  3. Getting started with neural networks
  4. Fundamentals of machine learning

    5. PART 2 – DEEP LEARNING IN PRACTICE

  5. Deep learning for computer vision
  6. Deep learning for text and sequences
  7. Advanced deep-learning best practices
  8. Generative deep learning
  9. Conclusions
  10. appendix A – Installing Keras and its dependencies on Ubuntu
  11. appendix B – Running Jupyter notebooks on an EC2 GPU instance

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