e_ameisen

10 ms·

31.
Most impactful AI trends of 2018: the rise of ML Engineering(medium.com)
2 points by e_ameisen 8y ago|1 comments
32.
Most impactful AI trends of 2018: the rise of ML Engineering(medium.com)
2 points by e_ameisen 8y ago|0 comments
33.
by e_ameisen 8y ago
Interesting idea, but seems to very much fall within the category of something you would often want to build in-house. I always imagined the right level of abstraction was closer to spacy's, a framework that lets you easily embed all t
34.
by e_ameisen 8y ago
Actually, you can click on the name of the attribute to "lock" it. That way you can change one without changing the other!
35.
Generating custom photo-realistic faces using AI(blog.insightdatascience.com)
11 points by e_ameisen 8y ago|0 comments
36.
by e_ameisen 8y ago
Co-author here. This post came out of a discussion with Adam, where we both realized that the advice we were giving to ML teams and ML Engineers to guide them to better results were very often process centric rather than model centric. Many
37.
by e_ameisen 8y ago
Co-author here. This is a surprisingly common situation. In fact starting with the simplest algo is usually the best way to prove the validity of your approach, and gather initial data to build a more complex model later. In addition, tryin
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How to deliver on Machine Learning projects(blog.insightdatascience.com)
2 points by e_ameisen 8y ago|0 comments
39.
by e_ameisen 8y ago
Very cool, especially the cropping feature! If anybody is curious as to how you would even start to tackle the problem, I wrote a tutorial to build your own prototype here: https://blog.insightdatascience.com/the-unreasonabl
40.
Preparing for the Transition to DevOps Engineering(blog.insightdatascience.com)
4 points by e_ameisen 8y ago|0 comments
41.
The Unreasonable Effectiveness of Deep Learning Representations(blog.insightdatascience.com)
9 points by e_ameisen 8y ago|1 comments
42.
A Journey Through Spark(blog.insightdatascience.com)
3 points by e_ameisen 8y ago|0 comments
43.
by e_ameisen 8y ago
Author here, this is aimed at an overview of the field, with links to relevant resources, but not a preview of courses we give at Insight since every project is different here!
44.
by e_ameisen 8y ago
A great book indeed, but a bit long for an introduction!
45.
Reinforcement Learning from scratch(blog.insightdatascience.com)
221 points by e_ameisen 8y ago|25 comments
46.
AI at massive scale, and Reinforcement Learning in industry(blog.insightdatascience.com)
4 points by e_ameisen 8y ago|0 comments
47.
by e_ameisen 9y ago
Agreed, we've also written about why the best use of your time is to start with a baseline (even in Computer Vision!) https://blog.insightdatascience.com/always-start-with-a-stup...
48.
Automated front-end development using deep learning(blog.insightdatascience.com)
3 points by e_ameisen 9y ago|0 comments
49.
Always start with a stupid model, no exceptions(blog.insightdatascience.com)
11 points by e_ameisen 9y ago|0 comments
50.
Always start with a stupid model, no exceptions(blog.insightdatascience.com)
2 points by e_ameisen 9y ago|0 comments
51.
Predicting e-sports winners with Machine Learning(blog.insightdatascience.com)
3 points by e_ameisen 9y ago|0 comments
52.
by e_ameisen 9y ago
Author here. If you have the means to label some data, this becomes quite an easy classification problem. Get multiple examples of posts, and labels (capitalist, libertarian, ...) for a group of users that represents your total population w
53.
by e_ameisen 9y ago
Author here. One of the reasons to start with something even simpler than embeddings such as fastText/Word2Vec, is their ease of explainability. As this blog post alludes to, the majority of ML work is data work, and in order to effici
54.
by e_ameisen 9y ago
Hi, Author here, happy to answer questions and share our vision of it. Many problems require more complex approaches, and we definitely have Fellows tackle some of those ( https://blog.insightdatascience.com/entity2vec-dad368
55.
How to solve most NLP problems: a step-by-step guide(blog.insightdatascience.com)
461 points by e_ameisen 9y ago|74 comments
56.
Deep Learning for Disaster Recovery(blog.insightdatascience.com)
3 points by e_ameisen 9y ago|0 comments
57.
NIPS 2017 – Day 3 Highlights(blog.insightdatascience.com)
2 points by e_ameisen 9y ago|0 comments
58.
NIPS 2017 – Day 2 Highlights(blog.insightdatascience.com)
3 points by e_ameisen 9y ago|0 comments
59.
NIPS 2017 – Day 1 Highlights(blog.insightdatascience.com)
2 points by e_ameisen 9y ago|0 comments
60.
Transform anything into a vector(medium.com)
2 points by e_ameisen 9y ago|0 comments

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