Browse Library
Sign In Start Free Trial
Close icon Search icon
Account
Browse Library Advanced Search Sign In Start Free Trial

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Java for Data Science
  • Toc
  • feedback
Java for Data Science

Java for Data Science

By : Richard M. Reese, Reese
close
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)
close
close
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
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
chevron up
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

Recurrent Neural Networks

RNN differ from feed-forward networks in that their input includes the input from the previous iteration or step. They still process the current input but use a feedback loop to take into consideration the inputs to the prior step, also called the recent past, for context. This step effectively gives the network memory. One popular type of recurrent network involves Long Short-Term Memory (LSTM). This type of memory improves the processing power of the network.

RNNs are designed to process sequential data and are especially useful for analysis and prediction with text data. Given a sequence of words, an RNN can predict the probability of each word being the next in the sequence. This also allows for text generation by the network. RNNs are versatile and also process image data well, especially image labeling applications. The flexibility in design and purpose and ease in training make RNNs popular choices for many data science applications. DL4J also provides support...

End of Section 7
Next Section
bookmark search playlist download
font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected
Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete