Microsoft CNTK Library v2.0 is Released

I’ve been so busy the last few days, that I didn’t even notice that Microsoft CNTK version 2.0 deep learning library was released on June 1, 2017. Very cool! See

The CNTK library contains very sophisticated, very powerful, machine learning functions. CNTK is written in C++ so that it can take advantage of GPUs, if available. But I prefer to use the CNTK Python language API rather than wrestling with C++.

To install CNTK with Python, you need an Anaconda distribution. I like the Anaconda version (4.1.1) that has Python 3.5. Once you have Anaconda/Python, you can use the Python PIP utility to install CNTK using a .whl file. In the image below, I use the –upgrade and –no-deps flags because I had an earlier version of CNTK installed. Note that I installed the CPU-only version because my desktop machine doesn’t have a GPU.

After I installed the 2.0 version of CNTK, I refactored one of the logistic regression examples I found in the CNTK documentation. CNTK is intended mostly for deep neural networks, but CNTK can do logistic regression too.

I’m very excited to explore CNTK version 2.0. I use both Google’s TensorFlow and Microsoft’s CNTK. I prefer CNTK — it just feels better to me. I know that’s subjective, and TensorFlow has a big lead over CNTK in terms of usage, but I’m going to place my bet on CNTK.

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