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This is very interesting. Wonderful visualizations! Any advice on how one gets started with building webpages similar to this?

That's a great suggestion, I'll see what I can do. Thank you for the feedback.
2 points by fontist 20 hours ago | link | parent | on: China's Role in the Federal Fund Rate

Great analysis! Could you perhaps make the font in the plots larger? It's quite difficult to read.

Thank you for the kind words.
3 points by listR 1 day ago | link | parent | on: China's Role in the Federal Fund Rate

Well done analysis. I had not heard of the custodial account connection before and found that intriguing.

Nice! Incredibly helpful tool
1 point by isseu 2 days ago | link | parent | on: Neural networks in retail industry

Not much information of how you can apply them to retail...

This topic has been discussed lately at work, thought it might be an interesting tool.

No Data Tau on the list?

Hope this information will help budget travelers to find cheap airline tickets easily

Amazing that you can manipulate 112 million rows of data. Is there a way for me to get access to that data or a similar set of data to practice building my own dashboard?

I'm not sure I agree with Python being better for External Libraries. While Python enjoys a huge host of library frameworks I don't think it compares to R in just the machine learning space.
1 point by aqny 14 days ago | link | parent | on: Word2Vec from scratch in Golang

[WIP] GloVe:

This is a tutorial-style post that offers an alternative approach to dealing with large sets of training data, without resorting to copying and moving files to hard-coded directories named `train`, `validation` and `test`. Instead, you keep the files where they "naturally" reside on your system and track their locations with a Pandas DataFrame, feeding their names to the Keras generator. It scales well when dealing with millions of image files and hundreds of gigabytes of data.


Nice post

Machine learning is not black-box. In this post, we try to better understand ML from a different way. Its essence is function estimation. Read the complete blog to better understand this.

Please feel free to get in touch with any questions or comments.

Richard Heyns Brytlyt CEO

Hello! I wrote this tutorial, and am happy to answer any questions about it. Thanks!
1 point by davidbrush 21 days ago | link | parent | on: Agile Data Science 2.0

I own and I am reading through the v1.0 currently. What are some of the differences in v2.0? One thing I was hoping for a little more detail on was the team process/governance side of things. For instance how to leverage scrum/kanban methodologies as part of the analytics development process as well as highlighting some of the key differences between analytics development and software development in the agile world.
1 point by davidbrush 21 days ago | link | parent | on: Agile Data Science 2.0

Shame that Oreilly has dropped DRM free downloads.

Hey, check out the work I did in natural language processing that went into the new Tasty App.
1 point by rjurney 22 days ago | link | parent | on: Agile Data Science 2.0

Yup, I wrote it!

Random forests have always been my favorite algorithm for reasons like easy understanding of its working, and it does not require many assumptions. RF is one of the first algorithms I go for when working on new datasets.

great solution!
1 point by jmsidhu 23 days ago | link | parent | on: Agile Data Science 2.0

Have you read it?

Might have been "comprehensive" if it described how to set this up yourself on your own servers instead of buying a 3rd party service. So, a simple intro, but not really comprehensive.

This is great. One of the best pieces of advice I ever heard for tackling algorithm interview questions was to remember that all algorithms follow naturally from data structures.

very cool. it would be nice to see some of the historical rating data annotated by major book releases. I notice that some in the top lists were published within those timespans. or maybe you explored that already and didn't see anything interesting.

Machine Learning is a new trend in IT world. Read your article & really liked.

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