This week, PyMC version v5.13.0 was released. PyMC is one of the main #Python ๐ libraries for ๐๐๐ฒ๐๐ฌ๐ข๐๐ง statistics โค๏ธ. It provides a framework for probabilistic programming, enabling users to build #Bayesian models with a simple Python API and fit them using ๐๐๐ซ๐ค๐จ๐ฏ ๐๐ก๐๐ข๐ง ๐๐จ๐ง๐ญ๐ ๐๐๐ซ๐ฅ๐จ (MCMC) methods ๐.
The new release includes new features, bug fixes ๐, and documentation improvements ๐. More details on the release notes ๐ ๐ #DataScience#machinelearning#statistics
(1/2) Models Demystified - A Practical Guide from t-tests to Deep Learning ๐๐๐ผ
The Models Demystified is a new book by Michael Clark and Seth Berry that focuses on the mechanizing of core data science algorithms. That includes the following topics:
โ Linear and logistic regression
โ Generalized Linear Models
โ Regularization methods
โ Model training approaches
โ Deep learning and neural networks
โ Causal Modeling
๐ฃ We are pleased sponsors of 26th April #R Dev Day at Imperial College #LDN. ๐๐ฌ๐
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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.