Weโre so excited to announce the support of survival analysis for time-to-event data across tidymodels!
โข The tidymodels framework is a collection of R packages for modeling and machine learning using tidyverse principles.
โข Survival analysis is now a first-class citizen in tidymodels, giving censored regression modeling the same flexibility and ease as classification or regression.
Whenever I see OpenAI's Sam Altman with his pseudo-innocent glance, he always reminds me of Carter Burke from Aliens (1986), who deceived the entire spaceship crew in favor of his corporation, with the aim of getting rich by weaponizing a newly discovered intelligent lifeform.
The Bandit Algorithms by Tor Lattimore and Prof. Csaba Szepesvยดari provides an introduction to the multi-armed bandit problem. This includes different approaches for solving this type of problems using stochastic, adversarial, and Bayesian frameworks.
Production Monitoring & Automations of LLM with LangSmith ๐ฆ๐๐ผ
LangChain released a crash course for LangSmith, their DevOps platform for deploying LLM applications into production. The course covers topics such as:
โ LLM applications monitoring
โ Setting automation
โ Performance monitoring
Andrej Karpathy just released a new repo with an implementation of training LLM with pure C/Cude with a few lines of code ๐. This repo, according to Andrej Karpathy, is still WIP, and the first working example is of GPT-2 (or the grand-daddy of LLMS ๐ ) ๐๐ผ
The Linear Algebra for Data Science course by Shaina Race Bennett provides a light and visual introduction to linear algebra โค๏ธ. The course focuses on the core linear operations and their data science applications:
โ Matrix operations
โ Least squares
โ Covariance
โ Linear regression
โ Eigenvalues and Eigenvectors
โ PCA
The Data Science for Beginner course by Microsoft provides, as the name implies, an introduction to data science. This ten-week course focuses on both theory and tools, such as:
โ Data structures
โ Statistics and probability
โ Python
โ Data wrangler
โ Data visualization
Neural Networks from Scratch in Python ๐๐๐ผ
The Neural Networks from Scratch in #Python ๐ course by Harrison Kinsley introduces neural networks by coding them from scratch. The course is based on Harrison's book (along with Daniel Kukiela), and it covers the following topics:
โ Core linear algebra and math operators
โ Neural network architecture
โ Different loss functions
โ Optimization and derivatives
Heute endet unsere Konferenz zu #MachineLearning. Wir danken allen Teilnehmenden!
Es gab spannende Beitrรคge, u.a. von Susanne Dandl von der Ludwig-Maximilians-Universitรคt Mรผnchen zum Thema interpretierbares #MaschinellesLernen oder Wesley Yung zu #DataScience bei Statistics Canada.
A few days ago, I posted about the Convex Optimization course by Prof. Stephen Boyd from Stanford University. Following this post, multiple people recommended checking the course book - Convex Optimization by Prof. Stephen Boyd and Prof. Lieven Vandenberghe.
Dans "La vie de laboratoire", classique de #sociologie des #science de Bruno Latour, une note en bas de pagelnous rappelle que le web scraping et les techniques de #MachineLearning ont finalement assez peu รฉvoluรฉs en 45 ans...
I don't have a GPU that'll run it, so I have no idea what it's like, but it deserves more attention for the effort. Boost for visibility if that's your thing?
Introduction to forecasting with ARIMA and seasonal ARIMA with Python ๐๐๐ผ
This short tutorial by Joaquรญn Amat Rodrigo and Javier Escobar Ortiz provides an introduction to forecasting with ARIMA models using Python ๐. This includes using different flavors of ARIMA methods from the statsmodels, pmdarima, skforecast, and statsForecast libraries.
That's the paper that introduced the #Transformer architecture, dispensing with recurrence and convolutions to achieve much faster training times and higher performance in a language task.