GPGPU Programming with CUDA for Color Space Conversion

General-purpose computing on graphics processing units (GPGPU, rarely GPGP) is the use of a graphics processing unit (GPU), which typically handles computation only for computer graphics, to perform computation in applications traditionally handled by the central processing unit (CPU) [1].
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What I Just Read : Assessing Cardiovascular Risk Factors with Computer Vision

One of my friend on Facebook, who happens to be a data scientist, shared a very exciting news. I do not click all the links that are shared by my friends on Facebook. But, this time I had to. The title was enough for any technology enthusiast to at least click for the details.

https://research.googleblog.com/
https://www.nature.com/articles/s41551-018-0195-0.pdf

Setting up Jupyter notebook with Tensorflow, Keras and Pytorch for Deep Learning

I was trying to set up my Jupyter notebook to work on some deep learning problem (some image classification on MNIST and imagenet dataset) on my laptop (Ubuntu 16.04 LTS). Previously I have used a little bit of Keras (which runs on top of Tensorflow) on a small dataset, but I did not use that with Jupyter. For that purpose I installed Tensorflow and Keras independently and used them in a Python script. However, it was not working from my Jupyter notebook. I googled for the solution, but found nothing concrete. I tried to activate the tensorflow environment and run jupyter notebook from their but in vein. I guess the reason is, I have downloaded different packages in different times and that might make some compatibility issues. Therefore, I decided to create a BRAND NEW conda environment for my deep learning endeavor. This is how it goes:
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