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Java Deep Learning Projects

You're reading from   Java Deep Learning Projects Implement 10 real-world deep learning applications using Deeplearning4j and open source APIs

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788997454
Length 436 pages
Edition 1st Edition
Languages
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Author (1):
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Md. Rezaul Karim Md. Rezaul Karim
Author Profile Icon Md. Rezaul Karim
Md. Rezaul Karim
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Table of Contents (13) Chapters Close

Preface 1. Getting Started with Deep Learning 2. Cancer Types Prediction Using Recurrent Type Networks FREE CHAPTER 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

Distributed deep learning across multiple GPUs

As stated earlier, we will see a systematic example for classifying a large collection of video clips from the UCF101 dataset using a convolutional-LSTM network. However, first we need to know how to distribute the training across multiple GPUs. In previous chapters, we discussed several advanced techniques such as network weight initialization, batch normalization, faster optimizers, proper activation functions, etc. these certainly help the network to converge faster. However, still, training a large neural network on a single machine can take days or even weeks. Therefore, this is not a viable way for working with large-scale datasets.

Theoretically, there are two main methods for the distributed training of neural networks: data parallelism and model parallelism. DL4J relies on data parallelism called distributed deep learning...

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