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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

Deep learning – computer vision – 6 projects

The following six Python deep learning projects, focusing on CV, represent the largest portion of the content of this book. We've already seen how some of the deep learning technologies we explore in detail, with reference to CV, have some applicability to other types of data, and in particular, to text-based data. In no small part, that is because of the enormous utility of CNNs in feature extraction and hierarchical representation. There is no magic tool that is perfect for all jobs—being a deep learning engineer in data science is no exception. But you should not underestimate the familiarity you'll get with CNNs, as you'll find yourself using them time and again, across many different datasets and business use cases. Being a data scientist without CNN skills is like being a carpenter without a...

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