MIT launched the 2024 edition of the Introduction to Deep Learning course by Prof. Alexander Amini and Prof.Ava Amini. The course started at the end of April and will run until June. The course lectures are published weekly. The course syllabus keeps changing from year to year, reflecting the rapid changes in this field.
(1/2) I recently posted a few posts about Rust 🦀 and my intention to leverage it for data science applications. Multiple people asked if Rust is a substitute for R or Python, and the short answer (in my opinion) is no. I see Rust as a complementary or supporting language that could make languages like R and Python faster.
Polaris 🐻❄️ is one example of a Python 🐍 application that uses Rust on the backend. 🧵👇🏼
Stanford University released a new course last week focusing on Deep Generative Models. The course, by Prof. Stefano Ermon, focuses on the models beyond GenAI models.
Want to check duplicate values across columns of a data.frame? Well you can do that in a basic way with TidyDensity and the check_duplicate_rows() function, or you can go through todays blog post for some other ideas with #BaseR#dplyr and #datatable
Join BetaNYC and the MTA Open Data team on June 5th at 9:30am to explore recently published MTA operating budget datasets. We’ll learn about the MTA’s open data program and conduct insightful analyses with their data.
(1/2) Google released a new foundation model for time series forecasting 🚀
The TimeFM (Time Series Foundation Model) is a foundation model for time series forecasting applications. This pre-trained model was developed by the Google Research team. It joins the recent trend of leveraging foundation models for time series forecasting, which includes Salesforce's Moirai and Amazon's Chronos.
(1/2) I have been following the work of @stevensanderson and David Kum for a few years now, and I am excited to see the release of their new book 🥳- Extending Excel with Python and R 🚀.
The book focuses on the common conjunction and collaboration between data scientists and Excel users. This includes scaling and automating #Excel tasks with #RStats and #Python and core data science applications such as data wrangling, working with APIs, data visualization, and modeling.
Join #PyData#Pittsburgh for a casual gathering of the local, national, and international PyData community on the sidelines of #PyCon US 2024! Meet up with fellow #DataScience, #MachineLearning, and scientific computing enthusiasts when the world's largest Python conference comes to town.
In the past few months, I created a bunch of Docker 🐳 tutorials covering random topics, from a fun setting for a Python 🐍 environment on the CLI to advanced topics such as multi-stage builds 🏗️. I organized all the tutorials under one folder, and I plan to keep updating this folder with future-related ones 😎.
Currently on my Docker tutorial TODO list:
➡️ Docker ENTRYPOINT vs CMD
➡️ Docker multi-architecture build
Day 4. Are Your Students Learning? How to check for learning as we’re teaching
Here are 5 exercises you can use as formative assessments when teaching coding skills, that will allow you to support your student at the moment when the learning is happening.
I've ended up with an inquiry from a #DataScience student interested in examples of #harm coming to people because of #data collection (especially in a war context, but open to anything). Do folks have any favorite pointers / examples I could pass along?
(1/2) Prompt Fuzzer - a new open-source project for LLM security 👇🏼
Prompt Fuzzer is a new open-source project that provides a set of functions for assessing the security of GenAI applications. This CLI-based tool enables you to run and test your system prompts to identify security vulnerabilities against potential dynamic LLM-based attacks.