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Java Deep Learning Projects

You're reading from  Java Deep Learning Projects

Product type Book
Published in Jun 2018
Publisher Packt
ISBN-13 9781788997454
Pages 436 pages
Edition 1st Edition
Languages
Toc

Table of Contents (13) Chapters close

Preface 1. Getting Started with Deep Learning 2. Cancer Types Prediction Using Recurrent Type Networks 3. Multi-Label Image Classification Using Convolutional Neural Networks 4. Sentiment Analysis Using Word2Vec and LSTM Network 5. Transfer Learning for Image Classification 6. Real-Time Object Detection using YOLO, JavaCV, and DL4J 7. Stock Price Prediction Using LSTM Network 8. Distributed Deep Learning – Video Classification Using Convolutional LSTM Networks 9. Playing GridWorld Game Using Deep Reinforcement Learning 10. Developing Movie Recommendation Systems Using Factorization Machines 11. Discussion, Current Trends, and Outlook 12. Other Books You May Enjoy

Answers to questions

Answer to question 1: We can train a YOLO network from scratch, but that would take a lot of work (and costly GPU hours). As engineers and data scientists, we want to leverage as many prebuilt libraries and machine learning models as we can, so we are going to use a pre-trained YOLO model to get our application into production faster and more cheaply.

Answer to question 2: Perhaps yes, but the latest DL4J release provides only YOLO v2. However, when I talked to their Gitter (see https://deeplearning4j.org/), they informed me that with some additional effort, you can make it work. I mean you can import YOLO v3 with Keras import. Unfortunately, I tried but could not make it workfullly.

Answer to question 3: You should be able to directly feed your own video. However, if it does not work, or throws any unwanted exception, then video properties such as frame rate...

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