Book Image

Java for Data Science

By : Richard M. Reese, Jennifer L. Reese
Book Image

Java for Data Science

By: Richard M. Reese, Jennifer L. Reese

Overview of this book

para 1: Get the lowdown on Java and explore big data analytics with Java for Data Science. Packed with examples and data science principles, this book uncovers the techniques & Java tools supporting data science and machine learning. Para 2: The stability and power of Java combines with key data science concepts for effective exploration of data. By working with Java APIs and techniques, this data science book allows you to build applications and use analysis techniques centred on machine learning. Para 3: Java for Data Science gives you the understanding you need to examine the techniques and Java tools supporting big data analytics. These Java-based approaches allow you to tackle data mining and statistical analysis in detail. Deep learning and Java data mining are also featured, so you can explore and analyse data effectively, and build intelligent applications using machine learning. para 4: What?s Inside ? Understand data science principles with Java support ? Discover machine learning and deep learning essentials ? Explore data science problems with Java-based solutions

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Table of Contents (19 chapters)
Java for Data Science
Java for Data Science
Credits
Credits
About the Authors
About the Authors
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Customer Feedback
Customer Feedback
Preface
Preface
Free Chapter
1
Getting Started with Data Science
Getting Started with Data Science
Problems solved using data science
Understanding the data science problem -  solving approach
Acquiring data for an application
The importance and process of cleaning data
Visualizing data to enhance understanding
The use of statistical methods in data science
Machine learning applied to data science
Using neural networks in data science
Deep learning approaches
Performing text analysis
Visual and audio analysis
Improving application performance using parallel techniques
Assembling the pieces
Summary
2
Data Acquisition
Data Acquisition
Understanding the data formats used in data science applications
Data acquisition techniques
Summary
3
Data Cleaning
Data Cleaning
Handling data formats
The nitty gritty of cleaning text
Cleaning images
Summary
4
Data Visualization
Data Visualization
Understanding plots and graphs
Creating index charts
Creating bar charts
Creating stacked graphs
Creating pie charts
Creating scatter charts
Creating histograms
Creating donut charts
Creating bubble charts
Summary
5
Statistical Data Analysis Techniques
Statistical Data Analysis Techniques
Working with mean, mode, and median
Standard deviation
Sample size determination
Hypothesis testing
Regression analysis
Summary
6
Machine Learning
Machine Learning
Supervised learning techniques
Unsupervised machine learning
Reinforcement learning
Summary
7
Neural Networks
Neural Networks
Training a neural network
Understanding static neural networks
Understanding dynamic neural networks
Additional network architectures and algorithms
Summary
8
Deep Learning
Deep Learning
Deeplearning4j architecture
Deep learning and regression analysis
Restricted Boltzmann Machines
Deep autoencoders
Convolutional networks
Recurrent Neural Networks
Summary
9
Text Analysis
Text Analysis
Implementing named entity recognition
Classifying text
Understanding tagging and POS
Extracting relationships from sentences
Sentiment analysis
Summary
10
Visual and Audio Analysis
Visual and Audio Analysis
Text-to-speech
Understanding speech recognition
Extracting text from an image
Identifying faces
Classifying visual data
Summary
11
Mathematical and Parallel Techniques for Data Analysis
Mathematical and Parallel Techniques for Data Analysis
Implementing basic matrix operations
Using map-reduce
Various mathematical libraries
Using OpenCL
Using Aparapi
Using Java 8 streams
Summary
12
Bringing It All Together
Bringing It All Together
Defining the purpose and scope of our application
Understanding the application's architecture
Data acquisition using Twitter
Understanding the TweetHandler class
Other optional enhancements
Summary

Chapter 8. Deep Learning

In this chapter, we will focus on neural networks, often referred to as Deep Learning Networks (DLNs). This type of network is characterized as a multiple-layer neural network. Each of these layers are rained on the output of the previous layer, potentially identifying features and sub-features of the dataset. A feature hierarchy is created in this manner.

DLNs typically work with unstructured and unlabeled data, which constitute the vast bulk of data found in the world today. DLN will take this unstructured data, identify features, and try to reconstruct the original input. This approach is illustrated with Restricted Boltzmann Machines (RBMs) in Restricted Boltzmann Machines and with autoencoders in Deep autoencoders. An autoencoder takes a dataset and effectively compresses it. It then decompresses it to reconstruct the original dataset.

DLN can also be used for predictive analysis. The last step of a DLN will use an activation function to generate output represented...

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