Medijski sadržaj
- Tweetovi
- Tweetovi i odgovori
- Medijski sadržaj, trenutna stranica.
-
Wow, the book went from best new release to best seller!!! Looks like the free first chapter helped some folks decide. If you are still on the fence, feel free to check the free PDF out below, the book is also currently 40% off! https://mlpowered.com/book/ pic.twitter.com/tEptKdYG7V
-
Last week the
@feedly team had me over to chat about some of the practical ML tips I’ve been writing about. The recording is available now. It’s a short video about why and how you should look at your data, including a slide copied from@karpathy :)https://youtu.be/xNGI0B_Sn1EPrikaži ovu nit -
-
My github handle is hundredblocks because of a
@waitbutwhy post that helped me plan my days. Most days you're awake for about 1000 minutes. That's one hundred 10-minute blocks. To prioritize, just decide how many of your blocks a given task is worth! https://waitbutwhy.com/2016/10/100-blocks-day.html …pic.twitter.com/69yb8pezFp
-
-
13 months. 250 pages. I wrote an ML book! Want to learn how to ship ML in practice? Check it out! Includes tips from
@WWRob,@mrogati,@cdubhland and more! It'll be out in winter & you can preorder it now. Amazon: http://bit.ly/mlpowered O'Reilly: http://bit.ly/mlpowered-oreilly …pic.twitter.com/PRD5PLfg5S
-
In ML and software, it often feels like your worst enemy is yourself. How often do you look back at code you've written and judge it harshly?
First you get frustrated at your past self, then you realize you only see the flaws because you've improved.
So satisfying!
pic.twitter.com/KkcXMVVZkD
-
A little bit ago I was on a panel with
@joelgrus,@seanjtaylor and@keunwoochoi to talk about sequential data. Amongst panelists, folks had experience in NLP, Audio, Computer Vision, and timeseries. It was fun to draw parallels between all those domains! https://www.infoq.com/presentations/panel-sequential-data/ …pic.twitter.com/dKG5deiGNA
-
From multi-stages approaches to DL. Thoughtful approach from http://x.ai . Surprised to see that defining the output as a set of sentences produces good results. One thing I've learned about DL: really hard to predict whether it will work https://x.ai/blog/the-unreasonable-effectiveness-of-neural-machine-translation-a-breakthrough-in-temporal-expression-understanding …pic.twitter.com/Nife6hKzFr
-
Hey! If this resonates with you, take a look at my book ( currently on early-release). It has many many more tips about building sane ML products! Amazon: http://bit.ly/mlpowered O'Reilly Early Release: http://bit.ly/mlpowered-oreilly …pic.twitter.com/waz777WUtB
Prikaži ovu nit -
Recommendation tutorials often cover matrix factorization. In practice, there are more business constraints to satisfy. For example, instead of predicting clothes a user will like, can you recommend a whole outfit?
@stitchfix_algo's approach is AWESOME https://multithreaded.stitchfix.com/blog/2019/07/09/simulacra-and-selection/ …pic.twitter.com/9coPRf2q3C
-
Wow,
@lyft built an ML system that automatically: Finds the best potential users to target. Allocates the right budget for each ad. Sets the right amount to bid on each platform to maximize the use of the budget. Thank you@seanjtaylor for the find! https://eng.lyft.com/lyft-marketing-automation-b43b7b7537cc …pic.twitter.com/uDg3lO0HFW
-
Bored of
@reddit? Here are a dozen@OpenAI GPT-2 bots trained on different subreddits arguing with each other Funny and scary example of how easy models pick up data biases And this isn't even the full model... https://www.reddit.com/r/SubSimulatorGPT2/ … Credit to@minimaxir for the find.pic.twitter.com/LbESVnxWUY
-
Hidden feedback loop? One way to take into account such effects being counterfactual evaluation (see this great talk on the subject https://www.youtube.com/watch?v=QWCSxAKR-h0 …).
-
When using ensembles to reduce variance, your models are literally reducing the variance of the error. I’ve never seen it explained quite as well as in this post by
@Chris_Said. Super clear! https://chris-said.io/2019/05/18/variance_after_scaling_and_summing/ …pic.twitter.com/YQZzj1481S
-
How do you build an ML pipeline for an actual business use case?
@WaleAkinfaderin focused on the data gathering, annotating and augmentation. He then used a well-understood model to show strong results. See the@InsightFellows blog for more https://blog.insightdatascience.com/end-to-end-object-detection-for-furniture-using-deep-learning-45a235f47a9a …pic.twitter.com/4TCU79j2Qr
-
How do you deploy cool models people can use? If you work at
@Google or@stripe, you can leverage their infrastructure. For all other Data Scientists,@InsightFellows is open sourcing One Click, to let you automatically deploy and test DS applications. https://blog.insightdatascience.com/automate-your-flask-deployments-on-aws-db4d8e2345a …pic.twitter.com/7B0ufpcQiw
-
Capturing data from users does not have to be a zero sum game. Get customers to label data in a way that is fun and directly beneficial to them! Give me
@stitchfix Style Shuffle over@Google Captcha any day. Bonus: how cool are those style embeddings? https://qz.com/quartzy/1603872/how-stitch-fixs-style-shuffle-learns-your-style/ …pic.twitter.com/G4A7V3g7Cq
-
Visualization of using k-means to build simple language models, and how historical tweets by friends can help predict a user's language! https://pudding.cool/2019/04/text-prediction/ …pic.twitter.com/tJE41th45U
-
Currently training a Q&A model, and it is producing crazy impressive results! Q: How do you find a good title? A: See attached None of the samples can be found in the training set that I used.




pic.twitter.com/EIco8cU47S
Čini se da učitavanje traje već neko vrijeme.
Twitter je možda preopterećen ili ima kratkotrajnih poteškoća u radu. Pokušajte ponovno ili potražite dodatne informacije u odjeljku Status Twittera.