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Deep Learning with Python

A Hands-on Introduction

  • Nikhil¬†Ketkar

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Nikhil Ketkar
    Pages 1-5
  3. Nikhil Ketkar
    Pages 7-16
  4. Nikhil Ketkar
    Pages 17-33
  5. Nikhil Ketkar
    Pages 35-61
  6. Nikhil Ketkar
    Pages 63-78
  7. Nikhil Ketkar
    Pages 79-96
  8. Nikhil Ketkar
    Pages 97-111
  9. Nikhil Ketkar
    Pages 113-132
  10. Nikhil Ketkar
    Pages 133-148
  11. Nikhil Ketkar
    Pages 149-158
  12. Nikhil Ketkar
    Pages 159-194
  13. Nikhil Ketkar
    Pages 195-208
  14. Nikhil Ketkar
    Pages 209-214
  15. Nikhil Ketkar
    Pages 215-222
  16. Back Matter
    Pages 223-226

About this book

Introduction

Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process.Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms.

This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included.

Deep Learning with Python also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments. 

You will:
  • Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe 
  • Gain the fundamentals of deep learning with mathematical prerequisites 
  • Discover the practical considerations of large scale experiments 
  • Take deep learning models to production

Keywords

Deep Learning Python Keras Theano Caffe Deep Learning Architecture GPU

Authors and affiliations

  • Nikhil¬†Ketkar
    • 1
  1. 1.BangaloreIndia

Bibliographic information

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