pyro
livebook
pyro | livebook | |
---|---|---|
9 | 80 | |
8,364 | 4,425 | |
0.5% | 2.1% | |
8.4 | 9.8 | |
10 days ago | 2 days ago | |
Python | Elixir | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
pyro
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Show HN: Designing Bridges with PyTorch
Mostly I use pytorch for statistical modeling https://pyro.ai . Under the hood that package uses a lot of Monte Carlo integration and variational methods (i.e. integration by optimization). It does support neural nets, but probably >80% of pyro users stick to simpler hierarchical Bayesian models.
- Pyro: The Universal, Probablistic Programming Language
- The Jupyter+Git problem is now solved
- Pyro: Deep universal probabilistic programming with Python and PyTorch
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Computational Bayesian Inference Techniques
Amortized Variational Inference (Like done in pyro.ai with neural networks)
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[P] torchegranate: a PyTorch rewrite of the pomegranate library for probabilistic modeling
Can you compare this to Pyro, which is also built on top of PyTorch?
- [Q] Updated book or review paper on MCMC methods
- Is anyone here working in uncertainty estimation in neural networks?
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[D] Do you train and deploy models using just one framework or multiple frameworks at work?
Using pyod, statmodels, scikit-learn, Tensorflow and pyro.ai (that is using PyTorch as backend). I always use the same framework for training and for production.
livebook
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Super simple validated structs in Elixir
To get started you need a running instance of Livebook
- Arraymancer – Deep Learning Nim Library
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Setup Nx lib and EXLA to run NX/AXON with CUDA
LiveBook site
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Interactive Code Cells
I prefer functional programming with Livebook[1] for this type of thing. Once you run a cell, it can be published right into a web component as well.
[1] - https://livebook.dev
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What software should I use as an alternative to Microsoft OneNote?
If you're a coder, Livebook might be worth a look too. I certainly have my eyes on it.
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Advent of Code Day 5
Would highly recommend looking at Jose's use of livebook to answer these. It makes testing easier. It's old but still relevant. Video link inside
- Advent of Code 2023 is nigh
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Racket branch of Chez Scheme merging with mainline Chez Scheme
That's hard to say. Racket is a rather complete language, as is F# and Elixir. And F# and Racket are extremely capable multi-paradigm languages, supporting basically any paradigm. Elixir is a bit more restricted in terms of its paradigms, but that's a feature oftentimes, and it also makes up for it with its process framework and deep VM support from the BEAM.
I would say that the key difference is that F# and Elixir are backed by industry whereas Racket is primarily backed via academia. Thus, the incentives and goals are more aligned for F# and Elixir to be used in industrial settings.
Also, both F# and Elixir gain a lot from their host VMs in the CLR and BEAM. Overall, F# is the cleanest language of the three, as it is easy to write concise imperative, functional, or OOP code and has easy asynchronous facilities. Elixir supports macros, and although Racket's macro system is far more advanced, I don't think it really provides any measurable utility over Elixir's. I would also say that F# and Elixir's documentation is better than Racket's. Racket has a lot of documentation, but it can be a little terse at times. And Elixir definitely has the most active, vibrant, and complete ecosystem of all three languages, as well as job market.
The last thing is that F# and Elixir have extremely good notebook implementations in Polyglot Notebooks (https://marketplace.visualstudio.com/items?itemName=ms-dotne...) and Livebook (https://livebook.dev/), respectively. I would say both of these exceed the standard Python Jupyter notebook, and Racket doesn't have anything like Polyglot Notebooks or Livebook. (As an aside, it's possible for someone to implement a Racket kernel for Polyglot Notebooks, so maybe that's a good side project for me.)
So for me, over time, it has slowly whittled down to F# and Elixir being my two languages that I reach for to handle effectively any project. Racket just doesn't pull me in that direction, and I would say that Racket is a bit too locked to DrRacket. I tried doing some GUI stuff in Racket, and despite it having an already built framework, I have actually found it easier to write my own due to bugs found and the poor performance of Racket Draw.
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Runme – Interactive Runbooks Built with Markdown
This looks very similar to LiveBook¹. It is purely Elixir/BEAM based, but is quite polished and seems like a perfect workflow tool that is also able to expose these workflows (simply called livebooks) as web apps that some functional, non-technical person can execute on his/her own.
1: https://livebook.dev/
- Livebook: Automate code and data workflows with interactive notebooks
What are some alternatives?
PyMC - Bayesian Modeling and Probabilistic Programming in Python
kino - Client-driven interactive widgets for Livebook
scikit-learn - scikit-learn: machine learning in Python
awesome-advent-of-code - A collection of awesome resources related to the yearly Advent of Code challenge.
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
interactive - .NET Interactive combines the power of .NET with many other languages to create notebooks, REPLs, and embedded coding experiences. Share code, explore data, write, and learn across your apps in ways you couldn't before.
trueskill - An implementation of the TrueSkill rating system for Python
Genie.jl - 🧞The highly productive Julia web framework
probability - Probabilistic reasoning and statistical analysis in TensorFlow
Elixir - Elixir is a dynamic, functional language for building scalable and maintainable applications
Keras - Deep Learning for humans
axon - Nx-powered Neural Networks