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Publicly shared solutions to Coding Challenges
Repository for the course Large Language Models and Generative AI for NLP
The best repository showing why transformers might not be the answer for time series forecasting and showcasing the best SOTA non transformer models.
brms R package for Bayesian generalized multivariate non-linear multilevel models using Stan
Free MLOps course from DataTalks.Club
LLM Zoomcamp - a free online course about real-life applications of LLMs. In 10 weeks you will learn how to build an AI system that answers questions about your knowledge base.
SWE-agent takes a GitHub issue and tries to automatically fix it, using GPT-4, or your LM of choice. It can also be employed for offensive cybersecurity or competitive coding challenges. [NeurIPS 2…
Material for SEG open-source webinar on March 19, 2024
This is a repository for the LinkedIn Learning course Python Object-Oriented Programming
Awesome-LLM: a curated list of Large Language Model
Neural Networks: Zero to Hero
Extra materials for *Fundamentals of Numerical Computation* by Driscoll and Braun.
Bayesian Data Analysis course at Aalto
The ChatGPT Retrieval Plugin lets you easily find personal or work documents by asking questions in natural language.
Dataframes powered by a multithreaded, vectorized query engine, written in Rust
Materials for a short course on convex optimization.
Scalable and user friendly neural 🧠 forecasting algorithms.
Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
Code repository for the online course Feature Engineering for Machine Learning
Feature Engineering for Machine Learning, Soledad Galli
Material workbench for the master-level course CS-E4740 "Federated Learning"
Data Engineering Zoomcamp is a free nine-week course that covers the fundamentals of data engineering.
My solution to the book A Collection of Data Science Take-Home Challenges
Examples and guides for using the OpenAI API
Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality.