Software Developer for as long as I can remember, building startups and projects. I've worked mostly in analytics, devops, and data science research. Running has proved useful so I enjoy doing it.
Self-plug: I also wrote a NLP library with sentiment analysis for Node.js. A lot of the concepts were used when I helped found a startup in social media analytics. Check it out!
In your README: It should be noted that most machine learning algorithms would be better suited in environments that can take advantage of many cores, such as in the case of GPU accelerated machine learning.
If you removed all dependencies for your library (or at least minimized the quantity) and bundled it for browser usage, web workers making use of concurrent JavaScript would be able to make this NLP library performant.
Since your focus is Node.js, that may not be practical - just a thought.
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Nice!
Self-plug: I also wrote a NLP library with sentiment analysis for Node.js. A lot of the concepts were used when I helped found a startup in social media analytics. Check it out!
github.com/nyxtom/salient/
Thanks Tom for sharing. nyxtom looks good
I think t building some interesting chatbot does not depend on the number of cores. More CPU can help, but I am sure, often node is fast enough.
Also with shared memory and workers available in node, the situation for more computation gets better.
For multi-core computing, check out piscina. It makes it w piece of cake.
In your README:
It should be noted that most machine learning algorithms would be better suited in environments that can take advantage of many cores, such as in the case of GPU accelerated machine learning.If you removed all dependencies for your library (or at least minimized the quantity) and bundled it for browser usage, web workers making use of concurrent JavaScript would be able to make this NLP library performant.
Since your focus is Node.js, that may not be practical - just a thought.