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  • Java for Data Science
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Java for Data Science

Java for Data Science

By : Richard M. Reese, Reese
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Java for Data Science

Java for Data Science

By: Richard M. Reese, 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
Table of Contents (13 chapters)
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Preface
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Free Chapter
1
1. Getting Started with Data Science
1. 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
2. Data Acquisition
2. Data Acquisition
Understanding the data formats used in data science applications
Data acquisition techniques
Summary
3
3. Data Cleaning
3. Data Cleaning
Handling data formats
The nitty gritty of cleaning text
Cleaning images
Summary
4
4. Data Visualization
4. 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
5. Statistical Data Analysis Techniques
5. Statistical Data Analysis Techniques
Working with mean, mode, and median
Standard deviation
Sample size determination
Hypothesis testing
Regression analysis
Summary
6
6. Machine Learning
6. Machine Learning
Supervised learning techniques
Unsupervised machine learning
Reinforcement learning
Summary
7
7. Neural Networks
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7. Neural Networks
Training a neural network
Understanding static neural networks
Understanding dynamic neural networks
Additional network architectures and algorithms
Summary
8
8. Deep Learning
8. Deep Learning
Deeplearning4j architecture
Deep learning and regression analysis
Restricted Boltzmann Machines
Deep autoencoders
Convolutional networks
Recurrent Neural Networks
Summary
9
9. Text Analysis
9. Text Analysis
Implementing named entity recognition
Classifying text
Understanding tagging and POS
Extracting relationships from sentences
Sentiment analysis
Summary
10
10. Visual and Audio Analysis
10. Visual and Audio Analysis
Text-to-speech
Understanding speech recognition
Extracting text from an image
Identifying faces
Classifying visual data
Summary
11
11. Mathematical and Parallel Techniques for Data Analysis
11. 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
12. Bringing It All Together
12. 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

Additional network architectures and algorithms

We have discussed a few of the most common and practical neural networks. At this point, we would also like to consider some specialized neural networks and their application in various fields of study. These types of networks do not fit neatly into one particular category, but may still be of interest.

The k-Nearest Neighbors algorithm

An artificial neural network implementing the k-NN algorithm is similar to MLP networks, but it provides significant reduction in time compared to the winner takes all strategy. This type of network does not require a training algorithm after the initial weights are set and has fewer connections among its neurons. We have chosen not to provide an example of this algorithm's implementation because its use in Weka is very similar to the MLP example.

This type of network is best suited to classification tasks. Because it utilizes lazy learning techniques, reserving all computation until after information has...

End of Section 5
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