Gwe Weka By B2c Video Download
Silje Bryd
While randomly searching on internet about Trainable 3D weka segmentation, I was directed to this site.
I am a student and still in the process of learning how to use ImageJ tool. I am a novice.
Till now, have understood how 2D segmentation works. Also there is a feature called 3D viewer which can show the image in 3D.
I tried opening one 2D image. I could see it as slice of a cylinder. (Imagine a chip from pringle potato chips box)
Then, I tried opening multiple such images to see if I can stack them on each other to see a 3D cylinder (Imagine a complete stack of chips in the Pringle chips box)
I am still not able to get the stacked 3D view.
To learn more about Lenovo Data Management solutions, please visit our Data Management solutions web page. More details on the WEKA Data Platform software can be found at weka.io. You can also contact your Lenovo sales representative or authorized channel partner.
The building blocks of Accelerated WEKA are packages like WekaDeeplearning4j and wekaRAPIDS (inspired by wekaPython). WekaDeeplearning4j (WDL4J) already supports GPU processing but has very specific needs in terms of libraries and environment configuration. WDL4J provides WEKA wrappers for the Deeplearning4j library.
For Python users wekaPython initially provided Python integration by creating a server and communicating with it through sockets. With this the user can execute scikit-learn ML algorithms (or even XGBoost) inside the WEKA workbench. Furthermore, wekaRAPIDS provides integration with RAPIDS cuML library by using the same technique in wekaPython.
The results for the wekaRAPIDS integration are shown, where we make a direct comparison between the baseline CPU execution with the Accelerated WEKA execution. The results for the WDL4J are shown in Table 5.
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