Book Image

Mathematica Data Analysis

By : Sergiy Suchok
Book Image

Mathematica Data Analysis

By: Sergiy Suchok

Overview of this book

There are many algorithms for data analysis and it’s not always possible to quickly choose the best one for each case. Implementation of the algorithms takes a lot of time. With the help of Mathematica, you can quickly get a result from the use of a particular method, because this system contains almost all the known algorithms for data analysis. If you are not a programmer but you need to analyze data, this book will show you the capabilities of Mathematica when just few strings of intelligible code help to solve huge tasks from statistical issues to pattern recognition. If you're a programmer, with the help of this book, you will learn how to use the library of algorithms implemented in Mathematica in your programs, as well as how to write algorithm testing procedure. With each chapter, you'll be more immersed in the special world of Mathematica. Along with intuitive queries for data processing, we will highlight the nuances and features of this system, allowing you to build effective analysis systems. With the help of this book, you will learn how to optimize the computations by combining your libraries with the Mathematica kernel.
Table of Contents (15 chapters)
Mathematica Data Analysis
About the Author
About the Reviewer

Image recognition

With the help of Mathematica's knowledge base on pattern recognition, we can engage in the development of artificial intelligence. Mathematica needs only one photo to report what is depicted in it. This is possible thanks to the ImageIdentify function:

However, this definition is not limited to only one category. Since it is not always possible to identify exactly what is depicted in an image, you should specify additional parameters to be able to select among options. For example, in this case, we are asking for 10 possible options to be shown:

If we know exactly that there is an edible fruit in the image, then Mathematica will help to classify it. For example, in the next case, we ask for 10 types of edible fruits, which could match this image together with their probabilities:

Note one very useful function—WordCloud. This allows you to build a cloud of words depending on their frequency, which helps to define what is in the image more clearly.

The ImageInstanceQ function...