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.

Preface

Who this book is for

What this book covers

To get the most out of this book

Free Chapter

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)

Answers to FAQs

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)

Answers to questions

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)

Answers to questions

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)

Answers to questions

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)

Answers to questions

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)

Answers to questions

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)

Answers to questions

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)

Answers to questions

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)

Answers to questions

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)

Answers to questions

Discussion, Current Trends, and Outlook

Discussion, Current Trends, and Outlook

Discussion and outlook

Current trends and outlook

Frequently asked questions (FAQs)

Answers to questions

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Neural Q-learning

Most reinforcement learning algorithms boil down to just three main steps: infer, do, and learn. During the first step, the algorithm selects the best action a in a given state s using the knowledge it has so far. Next, it performs an action to find the reward r as well as the next state s'.

Then it improves its understanding of the world using the newly acquired knowledge (s, r, a, s'). These steps can be formulated even better using QLearning algorithms, which is more or less at the core of Deep Reinforcement Learning.

Introduction to QLearning

Computing the acquired knowledge using (s, r, a, s') is just a naive way to calculate the utility. So, we need to find a more robust way to compute...