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1682487370925391872 | 4/ Different Interpretations 🤔
CI: "If we repeated this experiment 100 times, we'd expect the CI to contain the true parameter ~95 times."
CrI: "Given the data, there's a 95% probability that the true parameter lies within this interval." | https://twitter.com/i/web/status/1682487370925391872 | [
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1515358690081226761 | @WeatherStripApp if you like weather visualizations, https://t.co/zcAnhBmKof is another amazing tool that more people should know about https://t.co/YPIevpGtek | https://twitter.com/i/web/status/1515358690081226761 | [
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] |
1682504769204805633 | I've already recreated Edward Tufte’s New York City’s Weather in 2003 in @matplotlib...
https://t.co/GiTZrgoP4M
So I'd say it's about time to recreate it in @bokeh as well! See the first part of my upcoming blog post.
https://t.co/oVw0bnuI0K https://t.co/uyjiitcz49 | https://twitter.com/i/web/status/1682504769204805633 | [
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1682135179840704514 | This one falls into the extra spicy category so don’t do this if you haven’t been training your hamstrings and don’t try to do this one without warming up first. https://t.co/C3sNQGX1wT | https://twitter.com/i/web/status/1682135179840704514 | [
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] |
1682109194172878848 | New blog post: perspectives on diffusion, or how diffusion models are autoencoders, deep latent variable models, score function predictors, reverse SDE solvers, flow-based models, RNNs, and autoregressive models, all at once!
https://t.co/sBG8Waa9Ql | https://twitter.com/i/web/status/1682109194172878848 | [
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] |
1682010345177579523 | Lars maakte een supergedetailleerde metro-stijl kaart van het Belgische treinnetwerk: https://t.co/bhxDtUhjY2
(via https://t.co/ZVZk68MUcH) | https://twitter.com/i/web/status/1682010345177579523 | [
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1681700743693172738 | Thanks to everyone who came to my @EuroPython keynote on LLMs from prototype to production ✨
Here are my slides and a walkthrough of the talk as a Twitter thread 🧵
https://t.co/gNNtipUdNi https://t.co/mvqyCdsr8w | https://twitter.com/i/web/status/1681700743693172738 | [
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1682064328550236163 | Chiin is giving a lightning talk @EuroPython #EuroPython2023 about environmental conscious science education https://t.co/IiFWtob8wY | https://twitter.com/i/web/status/1682064328550236163 | [
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1682034962424537090 | As the number of #Python function arguments increases, it becomes challenging for developers to keep track of the purpose of numerous arguments and use the function.
To improve code readability, you can bundle multiple related arguments into a data structure with a dataclass. https://t.co/9nEK1LlqcW | https://twitter.com/i/web/status/1682034962424537090 | [
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1681676861443637251 | Check out the latest release of #Leafmap v0.23.0! 🌿🗺️ New features include interactive extraction and visualization of #AWS Open #Geospatial Data. 🌐🔍 Now you can easily extract sample images for your region of interest without downloading large amounts of data. 🙌 Perfect for… https://t.co/pDX5N6WKE1 | https://twitter.com/i/web/status/1681676861443637251 | [
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] |
1681663852256145410 | To get estimated prediction intervals for predictions made by a scikit-learn model, use MAPIE.
In the following code, we use MapieRegressor to estimate prediction intervals for a scikit-learn regressor.
https://t.co/SdHMlXozom
#Python https://t.co/UsvBKYgUeP | https://twitter.com/i/web/status/1681663852256145410 | [
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1681641346396835841 | LLAMA-v2 training successfully on Google Colab's free version! "pip install autotrain-advanced" 💥 Yes, you can also use your local machine! https://t.co/VOvocAQ46c | https://twitter.com/i/web/status/1681641346396835841 | [
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1681602528927170563 | This Post is from a suspended account. {learnmore} | https://twitter.com/i/web/status/1681602528927170563 | [
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] |
1681280868315193344 | LMQL now supports @ggerganov's excellent llama.cpp as (CPU-based) inference backend.
Seamlessly switch your query code from OpenAI to🤗 to llama.cpp.
This is possible due to our new token streaming protocol LMTP.
LMTP: https://t.co/zZ16urbUto
Docs: https://t.co/wWsQt9LJkq https://t.co/iKcYtbRsru | https://twitter.com/i/web/status/1681280868315193344 | [
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1681359703350333441 | Excited for Spy, a static python to WASM compiler. Amazing work from @antocuni
EuroPython Slides: https://t.co/ZCbjOfyYb2
Repo: https://t.co/31l965C6dm | https://twitter.com/i/web/status/1681359703350333441 | [
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1681378525830414338 | We're very excited to be one of the launch partners for Meta's Llama 2 🦙! We got to test Llama 2 in advance and were very impressed. The new version also has a much more permissive license. We've set everything up so you can run it on Databricks today. https://t.co/gf0OQZLBvZ | https://twitter.com/i/web/status/1681378525830414338 | [
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1681574687489368064 | Hiep, hiep, hoera voor onze favoriete entomoloog, Peter Berx (@Entomobiel)! 🥳 We zetten hem met plezier even in de bloemetjes (en bijtjes 😁) met deze video van zijn passage bij Vanavond Live met @xanderycke. Gefeliciteerd, Peter! https://t.co/l7AfxQjYSo | https://twitter.com/i/web/status/1681574687489368064 | [
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1681404748904431616 | Llama 2 has been released today, and of course I had to test it on my Mac 😇 !
This is the 7B chat model, converted to Core ML and running locally at ~6.5 tokens per second: https://t.co/jtP22mfCiY | https://twitter.com/i/web/status/1681404748904431616 | [
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1681551459970580480 | How to train LLAMA-v2 on custom dataset in just 50 lines of code: https://t.co/Uod2KxQKJS | https://twitter.com/i/web/status/1681551459970580480 | [
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1681503574814081025 | @simonw Yes: https://t.co/oBttZwKtpV
Getting 20-25 token/s with https://t.co/cS1t8gnUSi on M2 Max | https://twitter.com/i/web/status/1681503574814081025 | [
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] |
1681463685737246721 | These lines stick out from one of my favorite McKinsey studies:
1) "The role of industry in a company’s position is so substantial that you’d rather be an average company in a great industry than a great company in an average industry."
2) "In some cases, you’d rather be in… https://t.co/pDohzsYtI7 | https://twitter.com/i/web/status/1681463685737246721 | [
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1681395826684305425 | @pydatalondon @HelloFresh @pymc_labs @pymc_devs Notebook can be found here: https://t.co/wxJvXam8iG | https://twitter.com/i/web/status/1681395826684305425 | [
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1852289411855057016 | You can watch the most information-dense recording on retrieval in the RAG context this weekend.
This includes practical thinking around hybrid search, ColBERT, ColPali, and binary vectors, as well as how Ravenpack uses Vespa's SPANN implementation to scale to B of vectors.… | https://twitter.com/i/web/status/1852289411855057016 | [
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1849533633977369017 | I use the VLM to describe my screenshot using a structured output schema. https://t.co/oNUqZqxyEi | https://twitter.com/i/web/status/1849533633977369017 | [
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] |
1851973388106977779 | The Dutch DPA calls for input on the AI Act prohibition of AI systems on emotion recognition in the areas of workplace or education institutions. See https://t.co/0vykTXa8hY. https://t.co/RMot2rA9yG | https://twitter.com/i/web/status/1851973388106977779 | [
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1852138610985963973 | "An open-source, lightweight note-taking solution. The pain-less way to create your meaningful notes." https://t.co/sN8FOL9F7k | https://twitter.com/i/web/status/1852138610985963973 | [
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] |
1852135739947192604 | I’ve literally been telling image gen people for a few months now that, in my opinion, we should stop one-shotting single images, and instead create objects and layers, and have a « make consistent » model to blend them.
Looks like Blockade Labs is getting closer to what i want: https://t.co/vxgQiTZLBr | https://twitter.com/i/web/status/1852135739947192604 | [
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1851919137481609347 | Don’t sleep on Vision Language Models (VLMs).
With the releases of Llama 3.2 and ColQwen2, multimodal models are gaining more and more traction.
VLMs are multimodal models that can handle image and text modalities:
Input: Image and text
Output: Text
They can be used for many… https://t.co/DPW1ParoXa | https://twitter.com/i/web/status/1851919137481609347 | [
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1851721189133291888 | 🇪🇺 EU Policy Brief: "Harmonised Standards for the European AI Act" (7 pages) is the document every legal team should be reading, clarifying the role of standards in the EU AI Act. Download it below👇 https://t.co/Stq15tQjE9 | https://twitter.com/i/web/status/1851721189133291888 | [
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1851629939629375765 | Want to learn more about multiple imputation for #missingdata? This book by Stef van Buuren is the perfect guide! It addresses the often-overlooked topic of multiple imputation compared to univariate methods. Plus, it's🆓!
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1851663863026266263 | With the help of @hugovk we now have GitHub action powered by uv for pre-commit, https://t.co/sJNa7qzwoN (caching included), great to use whenever https://t.co/A7oZMSNhLX is not an option. | https://twitter.com/i/web/status/1851663863026266263 | [
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1851695007717290257 | Eugene created a game to help people LOOK AT THE DATA. It's simple but very effective in my experiments!
This is the tool we need.
https://t.co/f3qnIljdoA https://t.co/7rZho7AyB5 https://t.co/iHQnZSNbbB | https://twitter.com/i/web/status/1851695007717290257 | [
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] |
1851570743949090964 | My PyData Amsterdam talk is out!
The title is "Run a benchmark they said, it will be fun they said".
The talk is, as the name implies, about a benchmark.
Enjoy!
https://t.co/Aw1aULe6hP | https://twitter.com/i/web/status/1851570743949090964 | [
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] |
1846611547143262585 | Now all my projects (@FastAPI, Typer, SQLModel, Asyncer, etc) use uv to install packages in development and CI. 🚀
Much simpler, faster, clearer. ✨ | https://twitter.com/i/web/status/1846611547143262585 | [
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1851663992671842795 | @trickylabyrinth This is an all-timer
https://t.co/Ix36Ip6ddF | https://twitter.com/i/web/status/1851663992671842795 | [
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] |
1851645681150382103 | The most common mistakes I see teams make with LLM judges:
• Too many metrics
• Complex scoring systems
• Ignoring domain experts
• Unvalidated measurements
That's why I wrote this guide, w/ detailed examples to help people avoid these issues (1/4)
https://t.co/qWoti5QDvO | https://twitter.com/i/web/status/1851645681150382103 | [
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1851654159692616020 | Evaluating LLM output is hard. It's the bottleneck to scaling AI products for many teams
A key mistake is defining eval criteria w/o actually LOOKING AT THE DATA. This leads to irrelevant / unrealistic criteria + wasted effort
Thus, I built AlignEval https://t.co/vWdlRoctwQ | https://twitter.com/i/web/status/1851654159692616020 | [
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1851252352197480527 | ColPali is changing the game for PDF retrieval by eliminating the need for OCR and chunking methods 🚀
Inspired by ColBERT’s success with text, ColPali splits an image of a document into patches, which are then processed through a vision LLM called PaliGemma. The embeddings for… https://t.co/E2zwupDGQ8 | https://twitter.com/i/web/status/1851252352197480527 | [
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1851319613889359998 | Using VLMs for OCR https://t.co/2UzEjX2laK | https://twitter.com/i/web/status/1851319613889359998 | [
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1851322576728977533 | Trending new agent framework https://t.co/sBYPfEhvef | https://twitter.com/i/web/status/1851322576728977533 | [
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1851335124853408002 | Releasing Florence-2-DocLayNet-Fixed: Florence-2 modelf finetuned on the DocLayNet dataset.
To prevent the model from generating hallucinated class names, we re-mapped all class names to single tokens. This simple change brought 7% improvement of mAP50-95 score on the DocLayNet… https://t.co/qWiVH1Iqaa | https://twitter.com/i/web/status/1851335124853408002 | [
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] |
1681040273605029889 | 🦜🔗+Streamlit: Chat With Your Documents
Shout out to @meShashank93 for a FANTASTIC end-to-end example of using @LangChainAI and @streamlit to chat with your documents!
Code: https://t.co/45rZX6B4gG https://t.co/9CXI7yHbsu | https://twitter.com/i/web/status/1681040273605029889 | [
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] |
1681245990911725568 | In my latest article from the Writing Faster Python series, I've run a series of benchmarks for different tasks to see how much faster (or slower) it is to use pathlib instead of other functions.
https://t.co/uXEC9YgrkT | https://twitter.com/i/web/status/1681245990911725568 | [
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1680958393807167488 | I didn't get a chance to present my lightning talk at #SciPy2023, so thought I'd record and share it here
Declarative visualization in Python using @vega_vis Vega-Altair
https://t.co/LwDSYeZd48 | https://twitter.com/i/web/status/1680958393807167488 | [
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] |
1680584420648968192 | Our own @stefdegreef has fully covered the entire Belgian capital #Brussels by bike to create street-level imagery in 360° using one of our GoPro Hero Max's.
All footage is available on @mapillary .com under the CC-BY-SA license.
Massive applause👏👏👏
https://t.co/JrfLcNmj6j https://t.co/yOa0fYPCVm | https://twitter.com/i/web/status/1680584420648968192 | [
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1680676258156257283 | Object Detection and Image Classification with YOLO #KDnuggets https://t.co/eb96YIuJBj | https://twitter.com/i/web/status/1680676258156257283 | [
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] |
1679931131796307975 | GPT4All now supports Text Embeddings ⚡
- Generate text embeddings of arbitrary length documents for free on CPU at 8,000 tok/second.
- No external dependencies except C.
https://t.co/W3jvubmTyM https://t.co/4PnLwC15mQ | https://twitter.com/i/web/status/1679931131796307975 | [
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1680665487598837760 | The essay I just published called "How to Do Great Work" (https://t.co/zzxQTx7JJ3) grew out of a single paragraph in another essay I was writing. It seemed such an important topic that I cut it out and made it into its own essay. | https://twitter.com/i/web/status/1680665487598837760 | [
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1680505967606104064 | Coding tutorial on how to create a falcon (or any LLM) based PDF chatbot that runs locally without using langchain. Check it out here: https://t.co/LPb6j029Pn https://t.co/ipHDeRs3Hu | https://twitter.com/i/web/status/1680505967606104064 | [
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] |
1680190180102217729 | I finally found the time to wrap up the calmcode course on embeddings.
https://t.co/xEW6bPHJzs
The course starts by training letter embeddings from scratch and will use that experiment as a vehicle to explain how you might also get to multi-modal representations too. | https://twitter.com/i/web/status/1680190180102217729 | [
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] |
1679845079261822976 | Jupyter-scatter is a brand new library by @flekschas for producing, well, 2d scatter plots in a Jupyter notebook, but with amazing quality and easy to use:
https://t.co/1GHS4BMFjs
#SciPy2023 🤩 https://t.co/Ci391Ufao0 | https://twitter.com/i/web/status/1679845079261822976 | [
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] |
1679778530966601730 | Showing the trend gives us more to work with, shows us patterns, gives us real information.
💟 As always, Edward Tufte said it best:
"For non-data-ink, less is more.
For data-ink, less is a bore."
(6/6)
#informationdesign #datavisualization #powerfulcharts https://t.co/ZBeTfBPsdG | https://twitter.com/i/web/status/1679778530966601730 | [
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1679595929408610304 | The CEO of DeepMind, Demis Hassabis, went on @ezraklein to talk about the future of AI and science.
These are his book recommendations if you want to expand your mind and see what an AGI future looks like:
Permutation City, by Greg Egan — a wild story about how interesting and… https://t.co/iiBiZzbvlU | https://twitter.com/i/web/status/1679595929408610304 | [
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] |
1679521720179339264 | The magic of @duckdb: A spatial query (st_buffer) on a
@GeoParquet file in a GitHub repository in seconds... Goodbye to the database. https://t.co/82xZmZTYS9 | https://twitter.com/i/web/status/1679521720179339264 | [
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1679346323097636864 | Tutorial on using @duckdb with the Google Open Buildings @GeoParquet files on https://t.co/KKuNdQ9Qy6 https://t.co/IH1qGVc1MQ | https://twitter.com/i/web/status/1679346323097636864 | [
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] |
1679358632431853568 | this is wild — kNN using a gzip-based distance metric outperforms BERT and other neural methods for OOD sentence classification
intuition: 2 texts similar if cat-ing one to the other barely increases gzip size
no training, no tuning, no params — this is the entire algorithm: https://t.co/7mLIRlX48N https://t.co/IWe402RGgn | https://twitter.com/i/web/status/1679358632431853568 | [
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] |
1679145605766189057 | New in @scikit_learn -- experimental support for building models on GPUs with @PyTorch
- @thomasjpfan at #SciPy2023 https://t.co/3zdcykW95H | https://twitter.com/i/web/status/1679145605766189057 | [
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] |
1679143673693609985 | This is a wonderful overview of plotting with various Python libraries! 📈🐍
https://t.co/nUY8CvMlUw | https://twitter.com/i/web/status/1679143673693609985 | [
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] |
1679199371169251328 | ICYMI - there are now Azure Developer CLI Learn modules available!
✨Learn how the Azure Developer CLI can accelerate the time it takes for you to get your application from local development environment to Azure. ✨
https://t.co/PJNhwuB3m7 | https://twitter.com/i/web/status/1679199371169251328 | [
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1679013217182396419 | “Tour de France” by Jean-Jacques Sempé
The New Yorker magazine
July 12, 1999 https://t.co/r0bxBa8Lyr | https://twitter.com/i/web/status/1679013217182396419 | [
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1678754237520461829 | This for sure will make it into our @gisday activities! https://t.co/RMSn3zjRgY https://t.co/8US8Rc7zp3 | https://twitter.com/i/web/status/1678754237520461829 | [
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] |
1678879573486759937 | Recording of the 4-hour #geemap workshop at the SciPy Conference 2023
YouTube: https://t.co/oXeTT9dPgZ
Notebook: https://t.co/Bl5VFOJOEJ
#geospatial #EarthEngine #opensource #dataviz #SciPy2023 https://t.co/YfMuU2fBvZ | https://twitter.com/i/web/status/1678879573486759937 | [
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] |
1678777783848419330 | We're launching Keras Core, a new library that brings the Keras API to JAX and PyTorch in addition to TensorFlow.
It enables you to write cross-framework deep learning components and to benefit from the best that each framework has to offer.
Read more: https://t.co/xmmxBfSZgh https://t.co/k5K22UZNdR | https://twitter.com/i/web/status/1678777783848419330 | [
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1678691522630549506 | Training YOLOv5 object detection models has never been this easy.
Train your next model in 3 simple steps:
1. Download an object detection dataset from @roboflow Universe or use their annotation tool to create a custom dataset. Download in the YOLOv5 format.
2. Train the… https://t.co/daEfnROrIz | https://twitter.com/i/web/status/1678691522630549506 | [
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1678435054215655426 | The Right Way to Run Shell Commands From Python #python https://t.co/RvvcjFeyHL | https://twitter.com/i/web/status/1678435054215655426 | [
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1678071435011534850 | "That is one of the great shots we've seen here in YEARS!"
@AndreyRublev97, take a bow! 😱
#Wimbledon https://t.co/uEHcbcf1k8 | https://twitter.com/i/web/status/1678071435011534850 | [
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] |
1678317174354575361 | screencapture - take screenshots
taskpolicy - control scheduling of processes
say - text-to-speech engine
pmset - configure power management
networksetup - configure network settings
softwareupdate - manage OS updates
system_profiler - view system information | https://twitter.com/i/web/status/1678317174354575361 | [
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1677964429894320131 | Vive @LeTour! Vive la France! 🇨🇵
#GPSArt #StravaArt https://t.co/a668NspZxz | https://twitter.com/i/web/status/1677964429894320131 | [
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1678294912138113024 | Quick hacks on GitHub Pages https://t.co/hXBiVnWaNj | https://twitter.com/i/web/status/1678294912138113024 | [
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1678080934589997060 | Great article by @eugeneyan on the architecture of several big players search and recommendation system:
https://t.co/LRmcOWD0N5 | https://twitter.com/i/web/status/1678080934589997060 | [
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1678071778890117121 | What are the best Python-based LLMs for running on an M1/M2 Mac right now?
I'm particularly interested in models with weights that are available to download without having to click through a license or similar | https://twitter.com/i/web/status/1678071778890117121 | [
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] |
1678060204943097863 | When Deepmind needs semantic retrieval, they just use the largest index on the planet.
Fun fact: Query-doc similarity was done via simple TF-IDF instead of vectors. It performed better than vector retrieval when retrieve docs > 45 (they used 50).
https://t.co/7Y7urCaA95 https://t.co/UdSGaEIDqi | https://twitter.com/i/web/status/1678060204943097863 | [
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] |
1677805381089730563 | Happy to announce the 0.11.0 version of imbalanced-learn. This release makes sure to be fully compatible with scikit-learn 1.0+ with a couple of new improvements.
pip install -U imbalanced-learn
conda install -c conda-forge imbalanced-learn
Visit: https://t.co/X5yHQGkFXZ | https://twitter.com/i/web/status/1677805381089730563 | [
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1677972333825277954 | Here is a link to a Google Colab with the code: https://t.co/F3Gtd6JUGQ
Only thing you need to do is to use your OpenAI key. | https://twitter.com/i/web/status/1677972333825277954 | [
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1677727901842821122 | You may have seen this, but I bet all your followers haven’t yet.
They should. https://t.co/AxAjK0EAGQ | https://twitter.com/i/web/status/1677727901842821122 | [
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1677559271330856960 | @arpagon @jerryjliu0 This is the way to do it | https://twitter.com/i/web/status/1677559271330856960 | [
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1677306768769122304 | Need a color palette?
https://t.co/So75Vmw9s1 https://t.co/X1NiTcb7Wd | https://twitter.com/i/web/status/1677306768769122304 | [
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] |
1677277204793921537 | "An Introduction to Statistical Learning" in Python 🐍
A free pdf version is also available: https://t.co/YIhWmfwo9b https://t.co/ModE7yxLg3 | https://twitter.com/i/web/status/1677277204793921537 | [
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] |
1676980600656494594 | Goodhart's law is very real.
Reminded again of this super excellent post from @jaschasd on applying technical machine learning techniques to mitigate societal/product overfitting:
https://t.co/yRma5gQqW1 https://t.co/mNYVHyoxO6 | https://twitter.com/i/web/status/1676980600656494594 | [
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1676748983535013888 | My favorite feature in @LangChainAI are all the DocumentLoaders because it is a convenient data acquisition tool even outside of LLMs
Some notes: https://t.co/7cIgS56PWb | https://twitter.com/i/web/status/1676748983535013888 | [
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1676607003643224065 | The other suggestion was to add types and docstrings.
Personally don't think types buy us anything here but more typing 😉
Go crazy with docstrings and comments if you want. I have nothing against docstrings. https://t.co/KrBIPvGPMM | https://twitter.com/i/web/status/1676607003643224065 | [
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] |
1676606993123930115 | The next advice is to remove lambdas.
The complaint is that they make it hard to debug.
I don't agree with a "no lambda" policy. Removing them really complicates the comprehensions but ... 🤷♀️ https://t.co/c81xyD4oS3 | https://twitter.com/i/web/status/1676606993123930115 | [
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] |
1676582601291595777 | A little known fact is that conformal prediction has been originally developed for Support Vector Machines and the first published paper was called 'Learning by Transduction' (as Conformal Prediction was then called from later 1990s until 2005).
Support Vector Machines was the… https://t.co/K9eT5K3SDN | https://twitter.com/i/web/status/1676582601291595777 | [
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1676606977680572418 | I generally ask folks how they would write this code and I get advice but no actual takers on writing it "better".
So I've gone through the suggestions...
Remove "magic literals". I'm ok with this one.
Here's before and after: https://t.co/89x3Z97dAj | https://twitter.com/i/web/status/1676606977680572418 | [
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] |
1676253871868395523 | Oh and by the way, InstructBLIP directly comes with 4-bit and 8-bit inference thanks to the magic of @Tim_Dettmers, enabling same performance and reducing the amount of memory by half or more!!
Hat/tip to @younesbelkada for integrating 💪 https://t.co/XOq6Q09a9V https://t.co/bbxs7HJYB0 | https://twitter.com/i/web/status/1676253871868395523 | [
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1676357756058468352 | 🐍📰 The minimax algorithm can be used to find optimal strategies in many different games. In this tutorial, you'll learn to implement minimax in Python while playing the game of Nim. You'll also learn how you can make the algorithm more efficient
#python https://t.co/x1to1viVH8 | https://twitter.com/i/web/status/1676357756058468352 | [
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] |
1676270163283386371 | It's the 100th Data Vis Dispatch! We're celebrating just the way we like to — with lots of great charts and maps on everything from gender equality to hot dogs to the Loch Ness Monster 🥂
The party continues on our blog:
🎉 https://t.co/1T5ekOvlvS https://t.co/6nwrvgtZFm | https://twitter.com/i/web/status/1676270163283386371 | [
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] |
1675982820706226176 | one of the missing pieces of https://t.co/AY9kjOvQ7I falls into place - a python script to ship files in a folder to azure blob.
https://t.co/r6hO6W0D0q | https://twitter.com/i/web/status/1675982820706226176 | [
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] |
1675936797996511246 | Learning so much from Andrew Kelley's "Practical Data-Oriented Design" talk.
But also realizing how much I've learned since I started working on Ruff :)
https://t.co/39YzQAxrsv | https://twitter.com/i/web/status/1675936797996511246 | [
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] |
1675809284410441728 | *RGB no more: Minimally-decoded JPEG Vision Transformers*
by @jespark0 @jcjohnss #CVPR2023
You can directly train a Vision Transformer on JPEG data (no decoding), achieving SOTA results with much faster inference on JPEG inputs.
https://t.co/t76ug6fL5U https://t.co/PKU1A0Smc6 | https://twitter.com/i/web/status/1675809284410441728 | [
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Subsets and Splits