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1 point by lexda45 1 day ago | link | parent | on: Data Science Remote Jobs

It offers a lot of remote projects for data scientists.

How do you search for projects?


I created the Machine Learning Canvas to make it easier to ask the right questions at the beginning of an ML project, and to save people from wasting time and money due to a poor design of their ML system. I’m now releasing the first draft of a book that contains everything there is to know about this framework, in a 1-hour read.

The contest is now live! Unity3d.com/OTC

Haha, so the author goes to interviews to map interview processes? ;-)

Cold War v7

An introductory guide on how to do sales, revenue, conversion, etc. forecasting with Google Sheets only.

It's not clear to me why I would want to explore the hyperparameters in a web UI.

The goal here is to give to the ML community an open access, asynchronous, yet powerful, hyperparameters optimization tool. But it is just a Beta for now and we need feedback guys ! :)

The project is based on our open source optimization library (for now based on TPE-like): https://github.com/Dreem-Organization/benderopt

And you can interact with this library with a whole ecosystem of clients :

- A web client : directly on bender's website, you can visualize the optimization process on nice graphs; and compare the performances of different models on the same problem with a ranking board that ultimately allows you to pick the best model with the best hyperparameters set.

- A python one, a R one : it allows you to get automatic suggestions of hyperparameters set to test within your code.

Everything is documented on this readthedocs : https://bender-optimizer.readthedocs.io/en/latest/


did not know kubeflow let you do gpu

Hi! I wrote that post! Another friend pointed out yesterday this other post about Gaussian Processes: https://www.jgoertler.com/visual-exploration-gaussian-proces... I think that post has some fun visualizations for showing how different kernels work, but I tend to prefer my explanation. Would love to get more eyes on it and feedback specifically about whether I have any mistakes in there!
2 points by wminshew 81 days ago | link | parent | on: Emrys: p2p gpu compute marketplace

Hi -- founder here open to questions, currently looking for beta users ($10 in compute credits ~= 50 hours on a gtx 1080ti at current pricing) wiling to give feedback.

For users, emrys does ~4 things:

1. uploads a python script and requirements to the server with which a docker image is built

2. syncs the data set, if it exists locally, to the server

3. auctions the job’s execution to supplier’s meeting the user’s hardware requirements

4. streams output logs back to the user & downloads anything the python script saved in ./output/

More information can be found in the docs (https://docs.emrys.io) or by contacting support ([email protected])

1 point by jwkvam 91 days ago | link | parent | on: Matplotlib animations made easy

I was frustrated with how difficult I found making animations in matplotlib so I wrote something to make it easy and called it celluloid. I found the idea in plotnine and simply took out the plotnine specific code and generalized it some more (adding support for subplots).

The goal is that your visualization code shouldn't need to modified at all or as little as possible. With celluloid you take "photos" of your visualization to create each frame. Once all the frames have been captured you can create an animation with one call. The readme has more details.

I think the main audience for this is people who read the matplotlib animation tutorial (https://jakevdp.github.io/blog/2012/08/18/matplotlib-animati...) and thought it was still too complex.

I'm curious if you all think this is useful or how it could be improved. Thanks!

1 point by seoblogger 93 days ago | link | parent | on: Top 10 SEO Companies in USA

The Best Top 10 SEO Companies in the USA

> Important properties of the Normal distribution:

> * The total area under the curve is 1.

If you think this is a property of the "Normal distribution", rather than a property of distributions in general, let me go ahead and completely ignore your article.


https://www.cooldatasets.com is another one
1 point by tomtx 96 days ago | link | parent | on: Building AI & Busting Silos

Why there are just few real AI products for customers in finance?

Why do you think there are so few whiskeys combining smokiness with complexity?

Here's another interesting one https://www.reddit.com/r/algotrading/comments/9radvt/what_ar...
1 point by nischalm 109 days ago | link | parent | on: Lazy loading data in R

This is my first technical blog post! Please do read and give feedback. Thanks!

Spatial co-location pattern mining refers to the task of discovering the group of objects or events that co-occur at many places. Extracting these patterns from spatial data is very difficult due to the complexity of spatial data types, spatial relationships, and spatial auto-correlation. We model the co-location pattern discovery as a clique enumeration problem over a neighborhood graph (which is materialized using a distributed graph database). Further, we propose three new traversal based algorithms, namely CliqueEnumG, CliqueEnumK and CliqueExtend. These algorithms allow for a trade-off between time and memory requirements and support interactive data analysis without having to recompute all the intermediate results.

Listen on: iTunes/Apple Podcasts: http://bit.ly/df-apple Google Podcasts: http://bit.ly/df-google Spotify: http://bit.ly/df-spotify Stitcher: http://bit.ly/df-stitcher SoundCloud: http://bit.ly/df-sc Anchor: http://bit.ly/df-anchor Breaker: http://bit.ly/df-breaker Castbox: http://bit.ly/df-castbox Overcast: http://bit.ly/df-overcast Pocket Casts: http://bit.ly/df-pc Podbean: http://bit.ly/df-podbean Radio Public: http://bit.ly/df-rp Tune In: http://bit.ly/df-ti
1 point by pat 113 days ago | link | parent | on: Data job board

Maybe check out https://ai-jobs.net - it's super simple, mobile-friendly and has a filter to find jobs by region as well.
1 point by legallegends 116 days ago | link | parent | on: Online Trademark Registration

Trademark registration from Legal Legends helps establish ownership and protect brand of an entity. A trademark is a visual symbol, which may be a word, name, device, label or numerals used by a business to distinguish it goods or services from other similar goods or services originating from a different business. Register your brand and Logo in South Africa with the Online trademark registration service from Legal Legends.

Listen on: iTunes/Apple Podcasts: http://bit.ly/df-apple Google Podcasts: http://bit.ly/df-google Spotify: http://bit.ly/df-spotify Stitcher: http://bit.ly/df-stitcher SoundCloud: http://bit.ly/df-sc Anchor: http://bit.ly/df-anchor Breaker: http://bit.ly/df-breaker Castbox: http://bit.ly/df-castbox Overcast: http://bit.ly/df-overcast Pocket Casts: http://bit.ly/df-pc Podbean: http://bit.ly/df-podbean Radio Public: http://bit.ly/df-rp Tune In: http://bit.ly/df-ti

Dedicated Developers is a top-tier Web and Mobile Application Development company. The company was founded in 2007 and employs over 100 employees globally. Their industry leadership stems from their unique model that combines US-based project management and leadership with access to top talent in The UK, Philippines, and Argentina.
1 point by qnsi 126 days ago | link | parent | on: Data job board

Default sorting by name is useless. Also, I wish there was a way to check all job offers in Europe.
2 points by vvmisic 126 days ago | link | parent | on: Interpretable Optimal Stopping

Hi there -- one of the authors of the paper here. Optimal stopping problems constitute an important class of stochastic control problems, with many real applications, such as pricing financial options. Typically they are solved using approximate dynamic programming methods, which involve coming up with some approximation of the value function or the continuation value function.

In this paper, we take a different approach, where we represent the stopping policy as a tree, and propose a methodology for learning this tree from the data; so in the same way that one comes up with a tree for predicting a binary label in classification, or predicting a continuous value in regression, one obtains a tree that prescribes an action for each possible state. We show using a standard benchmark problem in option pricing that these tree policies perform very well, while being as simple and interpretable as tree models used in other areas of machine learning. We appreciate any questions or comments!


Listen on: iTunes/Apple Podcasts: http://bit.ly/df-apple Google Podcasts: http://bit.ly/df-google Spotify: http://bit.ly/df-spotify Stitcher: http://bit.ly/df-stitcher SoundCloud: http://bit.ly/df-sc Anchor: http://bit.ly/df-anchor Breaker: http://bit.ly/df-breaker Castbox: http://bit.ly/df-castbox Overcast: http://bit.ly/df-overcast Pocket Casts: http://bit.ly/df-pc Podbean: http://bit.ly/df-podbean Radio Public: http://bit.ly/df-rp Tune In: http://bit.ly/df-ti

Listen on: iTunes/Apple Podcasts: http://bit.ly/df-apple Google Podcasts: http://bit.ly/df-google Spotify: http://bit.ly/df-spotify Stitcher: http://bit.ly/df-stitcher SoundCloud: http://bit.ly/df-sc Anchor: http://bit.ly/df-anchor Breaker: http://bit.ly/df-breaker Castbox: http://bit.ly/df-castbox Overcast: http://bit.ly/df-overcast Pocket Casts: http://bit.ly/df-pc Podbean: http://bit.ly/df-podbean Radio Public: http://bit.ly/df-rp Tune In: http://bit.ly/df-ti

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