:gt: @graph_tool is a comprehensive and efficient :python: Python library to work with networks, including structural, dynamical and statistical algorithms, as well as visualization.
It uses :cpp: C++ under the hood for the heavy lifting, making it quite fast.
Here is a great summary or glossary doc about LLM by Aman Chadha. This long doc provides a summary of some of the main concepts related to LLM. This includes topics such as:
✅ Embeddings
✅ Vector database
✅ Prompt engineering
✅ Token
✅ RAG
✅ LLM performance evaluation
✅ Review main LLMs
I recently concluded 2 urban #datascience projects that I dreamt of doing since a long time, with 2 super talented MSc students who visited us @nerdsitu last year from Germany, Carlson @cbueth and Henrik @supergrobi.
@nerdsitu@cbueth@supergrobi Henrik studied "CoolWalks" - the potential for shaded walking, using building footprints and street networks from both synthetic and real cities. It is an intricate problem, but super important for climate adaptation, since using shadow from existing buildings is very effective but quite understudied.
I think in PowerShell and can manage in Python. I want to learn Rust to the degree I can write in it directly, rather than prototyping in PowerShell and then converting.
A lot of what I do is data manipulation and analysis. (Take several CSV files as input, and output new CSV files that answer business questions based on the inputs.) I'm seriously impressed with Rust's performance here.
If you've made this transition, advice on where to begin?
Version 1.7.1 of the NeuralForecast #Python library was released last month by Nixtla. The NeuralForecast library, as the name implies, provides a neural network framework for time series forecasting. 🧵👇🏼
(2/3) The release includes the support for the following new models:
✅ BiTCN - temporal convolutional networks forecasting model
✅ iTransformer - transformer-based forecasting model
✅ MLPMultivariate - an MLP model that supports multivariate tasks
In addition, the library now supports multi-node distributed training with Spark ✨ and Polars 🐻❄️ data frames 🚀
I'll be hosting Michigan Python tomorrow at 7pm EDT. Justin Smethers will be giving a talk about how DuckDB can be used to speed up your data analysis in Python. All are welcome online or in person. #Python#DataSciencehttps://meetu.ps/e/N1csW/t4QC5/i
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Request for testing/comment: can any #rstats users out there give the #wip {styler.equals} package a spin and kick its metaphorical tires 🚲 ? Install with remotes::install_github("robinlovelace/styler.equals") and see more at https://github.com/Robinlovelace/styler.equals Especially for those who prefer typing = over <- due to laziness or any other reason! Not intending to start a flame war 🔥 arrows are great too ⬅️ Any comments/suggestions welcome 🙏 #rspatial#DataScience#style