Hi guys! We are David and Pedro (aka @TheGurusTeam).
Just made a simple API serving a NLP model able to spot toxic comments and classify them as racist, insult, etc. (trained with Google/Jigsaw dataset).
We Finished the MVP today and wanted to share it with you. We are both Data Scientists and initiated this as a side project (weekends, holidays) while working full time, but have no idea about how to proceed and get our firsts customers. If you are interested, we can provide test API keys for free, just contact us ;-).
Our model is tuned for fast evaluation rather than being perfect but its results are pretty decent and constantly improving. There is a test form in the landing page so you can play with it as much as you want.
Thanks for your feedback in advance, and if you know someone who could benefit from this product, please share!
While I agree with a lot of the points that are made in the Nature article Scientists rise up against statistical significance (https://www.nature.com/articles/d41586-019-00857-9), I worry that they are changing one measure that can be abused for another.
Hey, we built amie-fern to address the version control and reproducibility issues from rapid prototyping with Jupyter notebooks. It is a Jupyter labs extension + web app that automatically tracks code, variables, data, and their dependencies in an interactive graph so you can explore your results and create a script that gets you back to any point in your workflow. Check out our short video, sign up for free, and try it! We'd love to hear what you think. Thanks 🙏!
Even though this is a post about joining an engineering team. I think it's just as relevant for data scientists. I've spent a significant amount of time working with data and I consider myself a data scientist as well.
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.
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.
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!