Andrej Karpathy just released a new repo with an implementation of training LLM with pure C/Cude with a few lines of code 🚀. This repo, according to Andrej Karpathy, is still WIP, and the first working example is of GPT-2 (or the grand-daddy of LLMS 😅) 👇🏼
The Linear Algebra for Data Science course by Shaina Race Bennett provides a light and visual introduction to linear algebra ❤️. The course focuses on the core linear operations and their data science applications:
✅ Matrix operations
✅ Least squares
✅ Covariance
✅ Linear regression
✅ Eigenvalues and Eigenvectors
✅ 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:
✅ Data structures
✅ Statistics and probability
✅ Python
✅ Data wrangler
✅ Data visualization
The Neural Networks from Scratch in #Python 🐍 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:
✅ Core linear algebra and math operators
✅ Neural network architecture
✅ Different loss functions
✅ Optimization and derivatives
Heute endet unsere Konferenz zu #MachineLearning. Wir danken allen Teilnehmenden!
Es gab spannende Beiträge, u.a. von Susanne Dandl von der Ludwig-Maximilians-Universität München 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 évolués 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 🚀👇🏼
This short tutorial by Joaquín Amat Rodrigo and Javier Escobar Ortiz provides an introduction to forecasting with ARIMA models using Python 🐍. 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.
If you had the feeling that the online discussion about COVID-19 vaccines was biased depending on the actors, you are right.
Using #MachineLearning and #NetworkScience we have shown that being a human or a bot, verified or unverified (according to previous Twitter rules) and political leaning were relevant factors for choosing the words in posts and, accordingly, the corresponding emotions to trigger.
A genuine computational social science study, led by Anna Bertani for her Msc thesis, now published also in collaboration with Riccardo Gallotti and Pierluigi Sacco
I have run out of severance and now I'm running out of savings. It's do or die now, folks. Job offers, job leads, job hunting advice...please send them all my way, and/or boost for reach. Freelancing is on the table, too. Details about me and what I'm looking for, to follow.
I'm very excited to announce that the Statistics Globe online course on "Principal Component Analysis (PCA): From Theory to Application in R" has just started.
(1/2) Moirai - Salesforce's Foundation Forecasting Model 🚀
Salesforce recently released Moirari - a new #Python 🐍 library with a foundation model for time series forecasting applications. According to the release blog - the model comes with universal forecasting capabilities and can handle multiple scenarios and different frequencies.
The below course by Dhaval Patel is a beginner-level course for Deep Learning in Python with Tensorflow 2.0 and Kares. The course covers the foundations of neural network and deep learning, which includes the following topics: 🧵👇🏼