kmax12

15 ms·

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by kmax12 8y ago
The challenge with machine learning today isn't that it doesn't work -- many organizations have successfully applied it to their problems -- but rather many people struggle to use it. There is massive potential if we can just make
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Boostrapping vs. Venture Financing(medium.com)
3 points by kmax12 8y ago|0 comments
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by kmax12 8y ago
Feature Labs, Inc. | Software Engineer | Boston, MA | Full-time | On-site or Remote | http://www.featurelabs.com Feature Labs is changing the way companies create new machine learning products and services. We make a web app and
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Google Dataset Search(toolbox.google.com)
1035 points by kmax12 8y ago|76 comments
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by kmax12 8y ago
Unlike most guides I've seen about ML, this one does a good job of focusing on developing and deploying a simple model first, then iterating. There are also lot of practical tips here, especially around feature engineering. > the se
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by kmax12 8y ago
Feature Labs, Inc. | Software Engineer | Boston, MA | Full-time | On-site | http://www.featurelabs.com Feature Labs is changing the way companies create new machine learning products and services. We make a web app and developer
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by kmax12 8y ago
A lot of easy to digest content in this! Always great to see quality free material to help more people pick up machine learning and get involved solving problems using data science. One thing I didn't see covered in depth here was feat
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by kmax12 8y ago
This seems like a great resource for understanding how machine learning algorithms actually work. Anyone reading the book to help them build more accurate models, might also be interested in supplementing it with more research into the impo
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by kmax12 8y ago
Feature Labs, Inc. | Software Engineer | Boston, MA | Full-time | On-site | http://www.featurelabs.com Feature Labs is changing the way companies create new machine learning products and services. We make a web app and developer
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by kmax12 8y ago
Yes, I think so. Featuretools is actually the core of my company's commercial product. Performance is tricky thing to answer. If you care about machine learning performance such as AUC, RSME, F1, then I think the answer would be 80%-90
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by kmax12 8y ago
one of the authors here. yes, that is definitely possible. it's explained in our doucmentation on deploying featuretools: https://docs.featuretools.com/guides/deployment.html
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by kmax12 8y ago
I agree that it is exactly this. New tooling has made machine learning easier to use. As a result, people with deep domain knowledge but less machine learning expertise are starting to apply ML to the problems they understand that best. One
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by kmax12 8y ago
> Lots of feature engineering based on domain expertise Exactly. This is what is required to make machine learning work well. For most people, this issue with machine learning isn’t that it doesn’t work but that it’s hard to use. I suspe
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by kmax12 9y ago
Feature Labs, Inc. | Software Engineer | Boston, MA | Full-time | On-site | http://www.featurelabs.com Feature Labs builds tools and API’s to deploy impactful machine learning solutions by combining open source software and prop
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by kmax12 9y ago
How does Ray compare to spark? Is there a reason to use spark once libraries like Dask or Ray become more mature?
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by kmax12 9y ago
10 seconds into the video of feature engineering they say that feature engineering takes up about 75% of the time https://developers.google.com/machine-learning/crash-course/... They understand the value, but but
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by kmax12 9y ago
I agree. I expect that as companies increase their focus on finding practical applications of ML / AI, the topic will start to get more attention in these tutorials, as well as from researchers. Right now, too many people assume you al
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by kmax12 9y ago
Great to see they have a nice introductory section to feature engineering! Feature engineering is often the most impactful thing you can do to improve quality of models and a place where I often see beginners (and experts for that matter) g
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by kmax12 9y ago
Feature Labs, Inc. | Software Engineer | Boston, MA | Full-time | On-site | http://www.featurelabs.com Feature Labs builds tools and API’s to deploy impactful machine learning solutions by combining open source software and prop
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by kmax12 9y ago
This from Google’s January launch of Clould AutoML. Related discussion here: https://news.ycombinator.com/item?id=16168098
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by kmax12 9y ago
Featuretools focuses on handling data with relational structure and timestamps. Here's an example to explain those two key points. Imagine you have a relational database from a retail store with tables for customers, transactions, prod
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by kmax12 9y ago
This appears to be an thorough overview of machine learning. Even though many bases are covered, I wish there was more on how to create or select features for machine learning in a systematic way. Feature extraction gets 1 paragraph, but fe
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by kmax12 9y ago
One crucial skill you will need is feature engineering. Formal methods for it aren’t typically in data science classes. Still, it’s worth understanding in order to build ML applications. Unfortunately, there aren't many available tools
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by kmax12 9y ago
Despite the claim to make AI accessible to every business, this release is fairly limited in that it only applies to images. We will have to see how they extend it going forward. Given the technology it's based on, I'd expect thin
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by kmax12 9y ago
One important skill you will need is feature engineering. Formal methods for it aren’t typically in ML ciriculums, but it’s worth understanding if you’re interested in applications if ML. Deep learning addresses it to some extent, but isn’t
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by kmax12 9y ago
Feature Labs, Inc. | Software Engineer | Boston, MA | Full-time | On-site | http://www.featurelabs.com Feature Labs builds tools and APIs to enable reliable and accurate data science automation. With our technology, users can di
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by kmax12 9y ago
Thanks for building this! I've been using it since it was posted as a show hn and have enjoyed the straightforward interface and steady improvements. Keep it up!
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by kmax12 9y ago
I worked on building a python library for automated feature engineering called Featuretools ( https://github.com/featuretools/featuretools/ ). I had been working on it for 2 years, but in 2017 we separated it from t
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by kmax12 9y ago
This is absolutely correct! After spending a lot of time trying to understand why building machine learning models is so difficult, I came to the same conclusion that "feature engineering" is the key to building high performing mo
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by kmax12 9y ago
I don't know of a definitive public resource for this. I published a paper in IEEE's Data Science and Advanced Analytics conference on it back in 2016. You can find that here: https://dai.lids.mit.edu/wp-content&#x

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