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Cancer Types Prediction Using Recurrent Type Networks

Large-scale cancer genomics data often comes in multiplatform and heterogeneous forms. These datasets impose great challenges in terms of the bioinformatics approach and computational algorithms. Numerous researchers have proposed to utilize this data to overcome several challenges, using classical machine learning algorithms as either the primary subject or a supporting element for cancer diagnosis and prognosis.

In this chapter, we will use some deep learning architectures for cancer type classification from a very-high-dimensional dataset curated from The Cancer Genome Atlas (TCGA). First, we will describe the dataset and perform some preprocessing such that the dataset can be fed to our networks. We will then see how to prepare our programming environment, before moving on to coding with an open source, deep learning library...

Key benefits

Understand DL with Java by implementing real-world projects

Master implementations of various ANN models and build your own DL systems

Develop applications using NLP, image classification, RL, and GPU processing

Description

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.

Who is this book for?

If you are a data scientist, machine learning professional, or deep learning practitioner keen to expand your knowledge by delving into the practical aspects of deep learning with Java, then this book is what you need! Get ready to build advanced deep learning models to carry out complex numerical computations. Some basic understanding of machine learning concepts and a working knowledge of Java are required.

What you will learn

Master deep learning and neural network architectures

Build real-life applications covering image classification, object detection, online trading, transfer learning, and multimedia analytics using DL4J and open-source APIs

Train ML agents to learn from data using deep reinforcement learning

Use factorization machines for advanced movie recommendations

Train DL models on distributed GPUs for faster deep learning with Spark and DL4J

Ease your learning experience through 69 FAQs

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

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Hands-On Artificial Intelligence with Java for Beginners

$25.99

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Jeffrey DeLong Mar 10, 2019

I am part way through this book and am learning a lot. In particular the examples are well thought out and real world. I have spent many hours going through them, as there is a lot to learn from them. I would recommend this book to anyone interested in deep learning with some experience with Java.

Amazon Verified review

Stefan Hildebrandt Jul 03, 2018

I've started using Java for DL projects and I had the opportunity to be one of the first readers of this book. A key insight for me was, that there are dozens of valuable examples and, considering that Java is usually not the first choice in this field, it's extremely helpful to have such a resource.

大村 Jul 19, 2019

Deep Learning for Java (DL4J) を紹介している点では、啓発的な本です。ただ難点は、サンプルコードが少ない点です。DL4J の環境設定や使用例(サンプルコード)があれば、良かったです。

Amazon Customer Jan 09, 2020

I was hoping for much from this book but it is littered with spelling mistakes and throwaway confusing lines because no definition of what is being discussed is given, or is given paragraphs later. Some diagrams also do not make sense. It became increasingly irritating. This book needs a significant revision.

Java Deep Learning Projects: Implement 10 real-world deep learning applications using Deeplearning4j and open source APIs

What do you get with Print?

Java Deep Learning Projects

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

Dataset preparation for training

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Java Deep Learning Projects: Implement 10 real-world deep learning applications using Deeplearning4j and open source APIs

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