Create and Test Machine-Learning Algorithims without Code
Gincker-AI playground makes ML model trainining as easy as using a calculator, and ML result delivery and sharing as simple as posting a Tweet.
With Gincker-AI playground, you can create and test a variety of machine learning algorithms, ranging from simple to sophisticated,
just by typing in a mathematic formula, pasting a dataset, or uploading a data file; you don’t need to write a single line of code or
rely on any special software package.
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Simple Interface: Gincker AI converts different machine learning models into templates. It implements a common interface and standardized input/output
format by encapsulating
and centralizing all the programming and implementation details internally. As long as you work through one template, you will be able
to use all the other templates to create and test your machine learning algorithms. This is because all templates on Gincker share the
same user interface and input/output formats. You can simply go to gincker.com, select a proper template, and create and test your desired
machine learning algorithm out of the box.
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Software as a Template: Over 40 templates on the current release of Gincker AI not only show the power and capability of the platform,
but also illustrate the procedure and format used in creating those machine learning models. These templates cover a broad range of
machine learning applications,
including classifications, regressions, supervised and unsupervised machine learning, neural networks for classification and
regression, convolutional neural netwroks for handwriting recognition, self-organization maps for color clustering, traveling salesman problem, etc.
In addition, more templates will be added to Gincker over time.
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Customization: Gincker AI provides rich configuration options that allows you to customize your output from trained machine learning models. For example,
Gincker provides 13 different
categorical color schemes that can be used to specify colors for different chart series.
Gincker also consists of over 100 different colormaps that define the color schemes for various types of visualizations, such as surface
and heatmap charts. You can also customize the other elements for your result charts, such as chart title, axis, gridline, background color,
number of grid points, etc.
Output from Gincker AI - A Unique URL Link
Gincker AI allows you to save your work to gincker - a unique URL link.
The saved gincker can not only provide output results, but also bring the development environment directly to users.
Users can use the gincker to regenerate, manipulate, modify, and customize the machine learning algorithms
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Dynamic Results: The output from Gincker is a URL link called gincker. This gincker can be embedded in websites, incorporated into applications,
or integrated into research papers. Users can also bookmark the gincker for later use or share it with friends, colleagues, or other users. The
advantage of ginckers over the static output results from traditional software packages is that a gincker can bring the development environment
to users and deliver dynamic content - users can use gincker to regenerate, manipulate, modify, or customize the results to meet their requirements.
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Effective Communication Tool: The gincker has an extremely small file size - it is simply a single line of text with the format:
"https://ai.gincker.com/{template}#{xxxxxxxxxx}", which is shorter than a Tweet.
It consists of three parts: gincker.com, which is our website name, the template name, and ten randomly generated characters that are used to identify the gincker.
Therefore, exchanging ginckers over the Internet will be much faster and more efficient than thansmitting large graphics or image files. The gincker
links will become an effective communication tool in our daily life.
You can learn more about Gincker-AI by clicking on the above link button.