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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

CNN architecture

In CNN networks, the way connectivity is defined among layers is significantly different compared to MLP or DBN. The convolutional (conv) layer is the main type of layer in a CNN, where each neuron is connected to a certain region of the input image, which is called a receptive field.

To be more specific, in a CNN architecture, a few conv layers are connected in a cascade style: each layer is followed by a rectified linear unit (ReLU) layer, then a pooling layer, then a few more conv layers (+ReLU), then another pooling layer, and so on. The output from each conv layer is a set of objects called feature maps, which are generated by a single kernel filter. Then, the feature maps are fed to the next layer as a new input. In the fully connected layer, each neuron produces an output followed by an activation layer (that is, the Softmax layer):

A conceptual architecture...
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