Genuinely asking, why is it considered bad? If it is done right, it will guide the customer better on whether they want something like it. We all see the photos/reviews on google maps before going inside a restaurant.
I read this quote somewhere: How you do something is how you do everything. I go by this principle in my life. Lately, I have been thinking in how many cases will this be not true.
I feel that this is a data collection activity (and thus, more advanced future models and usecases) disguised as a social media. People will provide feedback in the form of clicks/views on AI generated content (better version of RLHF) on unverified/subjective domains.
Biggest problem OpenAI has is not having an immense data backbone like Meta/Google/MSFT has. I think this is step in that direction -- create a data moat which in turn will help them make better models.
Hey, cool idea. Would you be able to tell me the tech stack for the whole app? I want to build a similar application for some other use case. I have built a static map with all my labels using leaflet in Python. To turn it into something like you have, what technologies will I need?
I want to learn more about how to rebalance my portfolio. I started with ETFs and MFs and then bought some good stocks when they were low. But I have never rebalanced it. Would you be able to share some resources about it? Also, if possible, some pointers about your script.
I noticed that the two bars were breaking differently under the hydraulic press. One was crumbling and the other (manufactured) was exploding. There was no mention of this effect in the video. It couldn't be the due to force because in the 2nd half the manufactured bar broke at a lower force. Could this factor has consequences on how manufactured sand concrete behaves with natural phenomenon (hurricanes, earthquakes, fires, etc.)
I could never put it words like in the article, but I always thought this was true (not just to books, but to all the inputs). Many times in my life, I have remembered obscure stuff from a random movie or specific parts of a conversation that I had many years back. Similarly with books, I can't recall it completely, but the sense of it is something that easily pops up.
Now I have started taking advantage of this using Anki. Whenever I read something having a particularly interesting or thought-provoking idea, I find a way to create flashcard(s) out of it. Now I am able to recall these things more often and many times they have guided me in my life or helping out friends.
Domain: Recommendation Systems, NLP (Translation, Language Modeling),
Classification Algorithms, Regression Algorithms, Deep Learning (Seq2Seq, Transformer, RNN, LSTM, GRU, CNN), Data Scraping, Visualization (matplotlib, ggplot2).
I am Senior Data Scientist with ~8 years of experience in Data Science. For the last three years, I have been working on Recommendation Engines for a B2C company. I have deployed 30+ models in production, receiving more than 400k QPS traffic. I have seen e2e journey of multiple data science projects: business problem -> formulation -> offline training/eval -> deployment -> A/B testing -> Scaling up -> Monitoring. In my new role, I am looking to apply my experience to design e2e ML systems.
Great work. Nice blend of aesthetics and simplicity of building the timeline.
Along with embed link, I was wondering if there is a way to download the HTML of the rendered timeline directly. I use static pages for my blog and would love a way to add the rendered output directly.
I write about random experiments I do in my life (data science, personal analytics, reviews, travel, etc). Been travelling a lot this year, but haven't been able to write about it much. Hoping to change that in the next few weeks.
I recently created an open source website[1] having a collection of podcasts about Machine Learning, Data Science, and ML Engineering. The podcast details are updated daily using GitHub actions. The source is available at the GitHub Repo[2]. Adding a new podcast is easy: just add a simple text file with YAML front matter. I want the website to become a go-to place where people find the next Data Science podcast they want to listen to. I have plans to make podcast pages more informative. My objectives of sharing the website are three-fold:
1. Share this resource with people here who might be interested in podcasts.
2. Feedback and suggestions about what more information can be added to make the discovery and selection process for users easier.
3. If people have any additions to the current list, I am happy to add them.
Hope this helps the community. People on r/machinelearning[3] liked it.
Hi All,
I recently created an open source website[1] having a collection of podcasts about Machine Learning, Data Science, and ML Engineering. The podcast details are updated daily using GitHub actions. The source is available at the GitHub Repo[2]. Adding a new podcast is easy: just add a simple text file with YAML front matter. I want the website to become a go-to place where people find the next Data Science podcast they want to listen to. I have plans to make podcast pages more informative. My objectives of sharing the website are three-fold:
1. Share this resource with people here who might be interested in podcasts.
2. Feedback and suggestions about what more information can be added to make the discovery and selection process for users easier.
3. If people have any additions to the current list, I am happy to add them.
Hope this helps the community. People on Reddit r/machinelearning[3] liked it.
This sounds very similar to what Design Thinking suggests. Connect with your (potential) customers to get their habits and preferences so you've more ideas and then prototype them through mockups or small models and once you've the feedback from the customers, decide to either go ahead with the version 2, pivot it, or shelf it.
Design thinking talks about being empathetic to your users/customers. Which also sounds like the essence of this post.
The site looks good. Kudos. I was looking for an (android) app that does this. All the apps that I tried were so bad or filled with unnecessary things that I gave up on tracking the time for now.
Check out MusicBRainz. have done a pretty good job of the tools around music tagging. Following is the link of the similar tool you are woking on from these guys: https://picard.musicbrainz.org/
Thanks. You can find the notebook here: https://github.com/TrigonaMinima/Notebooks with the name (Gradient Descent - Maximum Area.ipynb). Because of the embedded plots and gifs it's big in size so it's takes time to render on GH
Email: shivam underscore r at outlook