Book Image

Java Deep Learning Projects

Book Image

Java Deep Learning Projects

Overview of this book

Java is one of the most widely used programming languages. With the rise of deep learning, it has become a popular choice of tool among data scientists and machine learning experts. Java Deep Learning Projects starts with an overview of deep learning concepts and then delves into advanced projects. You will see how to build several projects using different deep neural network architectures such as multilayer perceptrons, Deep Belief Networks, CNN, LSTM, and Factorization Machines. You will get acquainted with popular deep and machine learning libraries for Java such as Deeplearning4j, Spark ML, and RankSys and you’ll be able to use their features to build and deploy projects on distributed computing environments. You will then explore advanced domains such as transfer learning and deep reinforcement learning using the Java ecosystem, covering various real-world domains such as healthcare, NLP, image classification, and multimedia analytics with an easy-to-follow approach. Expert reviews and tips will follow every project to give you insights and hacks. By the end of this book, you will have stepped up your expertise when it comes to deep learning in Java, taking it beyond theory and be able to build your own advanced deep learning systems.

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Table of Contents (13 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Getting Started with Deep Learning
Getting Started with Deep Learning
A soft introduction to ML
Delving into deep learning
Artificial Neural Networks
ANNs and the backpropagation algorithm
Neural network architectures
DL frameworks and cloud platforms
Deep learning from a disaster – Titanic survival prediction
Frequently asked questions (FAQs)
Summary
Answers to FAQs
2
Cancer Types Prediction Using Recurrent Type Networks
Cancer Types Prediction Using Recurrent Type Networks
Deep learning in cancer genomics
Cancer genomics dataset description
Preparing programming environment
Cancer type prediction using an LSTM network
Frequently asked questions (FAQs)
Summary
Answers to questions
3
Multi-Label Image Classification Using Convolutional Neural Networks
Multi-Label Image Classification Using Convolutional Neural Networks
Image classification and drawbacks of DNNs
CNN architecture
Multi-label image classification using CNNs
Frequently asked questions (FAQs)
Summary
Answers to questions
4
Sentiment Analysis Using Word2Vec and LSTM Network
Sentiment Analysis Using Word2Vec and LSTM Network
Sentiment analysis is a challenging task
Using Word2Vec for neural word embeddings
Datasets and pre-trained model description
Sentiment analysis using Word2Vec and LSTM
Frequently asked questions (FAQs)
Summary
Answers to questions
5
Transfer Learning for Image Classification
Transfer Learning for Image Classification
Image classification with pretrained VGG16
Developing an image classifier using transfer learning
Making simple inferencing
Frequently asked questions (FAQs)
Summary
Answers to questions
6
Real-Time Object Detection using YOLO, JavaCV, and DL4J
Real-Time Object Detection using YOLO, JavaCV, and DL4J
Object detection from images and videos
You Only Look Once (YOLO)
Developing a real-time object detection project
Frequently asked questions (FAQs)
Summary
Answers to questions
7
Stock Price Prediction Using LSTM Network
Stock Price Prediction Using LSTM Network
State-of-the-art automated stock trading
Developing a stock price predictive model
Frequently asked questions (FAQs)
Summary
Answers to questions
8
Distributed Deep Learning – Video Classification Using Convolutional LSTM Networks
Distributed Deep Learning – Video Classification Using Convolutional LSTM Networks
Distributed deep learning across multiple GPUs
Video classification using convolutional – LSTM
Distributed training on AWS deep learning AMI 9.0
Frequently asked questions (FAQs)
Summary
Answers to questions
9
Playing GridWorld Game Using Deep Reinforcement Learning
Playing GridWorld Game Using Deep Reinforcement Learning
Notation, policy, and utility for RL
Neural Q-learning
Developing a GridWorld game using a deep Q-network
Playing the GridWorld game
Frequently asked questions (FAQs)
Summary
Answers to questions
10
Developing Movie Recommendation Systems Using Factorization Machines
Developing Movie Recommendation Systems Using Factorization Machines
Recommendation systems
Factorization machines in recommender systems
Developing a movie recommender system using FMs
Frequently asked questions (FAQs)
Summary
Answers to questions
11
Discussion, Current Trends, and Outlook
Discussion, Current Trends, and Outlook
Discussion and outlook
Current trends and outlook
Frequently asked questions (FAQs)
Answers to questions
12
Other Books You May Enjoy
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Frequently asked questions (FAQs)

Now that we have solved the GridWorld problem, there are other practical aspects in reinforcement learning and overall deep learning phenomena that need to be considered too. In this section, we will see some frequently asked questions that may be already on your mind. Answers to these questions can be found in Appendix.

  1. What is Q in Q-learning?
  2. I understand that we performed the training on GPU and cuDNN for faster convergence. However, there is no GPU on my machine. What can I do?
  3. There is no visualization, so it is difficult to follow the moves made by the agent toward the target.
  4. Give a few more examples of reinforcement learning.
  5. How do I reconcile the results obtained for our mini-batch processing?
  6. How would I reconcile the DQN?
  7. I would like to save the trained network. Can I do that?
  8. I would like to restore the saved (that is, trained...
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End of Section 6
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