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Python Deep Learning Projects

You're reading from   Python Deep Learning Projects 9 projects demystifying neural network and deep learning models for building intelligent systems

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Product type Paperback
Published in Oct 2018
Publisher Packt
ISBN-13 9781788997096
Length 472 pages
Edition 1st Edition
Languages
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Authors (3):
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Rahul Kumar Rahul Kumar
Author Profile Icon Rahul Kumar
Rahul Kumar
Matthew Lamons Matthew Lamons
Author Profile Icon Matthew Lamons
Matthew Lamons
Abhishek Nagaraja Abhishek Nagaraja
Author Profile Icon Abhishek Nagaraja
Abhishek Nagaraja
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Toc

Table of Contents (17) Chapters Close

Preface 1. Building Deep Learning Environments 2. Training NN for Prediction Using Regression FREE CHAPTER 3. Word Representation Using word2vec 4. Building an NLP Pipeline for Building Chatbots 5. Sequence-to-Sequence Models for Building Chatbots 6. Generative Language Model for Content Creation 7. Building Speech Recognition with DeepSpeech2 8. Handwritten Digits Classification Using ConvNets 9. Object Detection Using OpenCV and TensorFlow 10. Building Face Recognition Using FaceNet 11. Automated Image Captioning 12. Pose Estimation on 3D models Using ConvNets 13. Image Translation Using GANs for Style Transfer 14. Develop an Autonomous Agent with Deep R Learning 15. Summary and Next Steps in Your Deep Learning Career 16. Other Books You May Enjoy

Summary and Next Steps in Your Deep Learning Career

This has been a fantastic journey and you've been quite productive as a member of the team! We hope that you've enjoyed our practical approach to teaching Python Deep Learning Projects. Furthermore, it was our intention to provide you with thought-provoking and exciting experiences that will further your intuition and form the technical foundation for your career in deep learning engineering.

Each chapter was structured similarly to participating as a member of our Intelligence Factory team, where, by going through the material, we achieved the following:

  • Saw the big picture of the real-world use case and identified the success criteria
  • Got focused and into the code, loaded dependencies and data, and built, trained, and evaluated our models
  • Expanded back out to the big picture to confirm that we achieved our goal
...
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