Natural Gradient Boosting for Probabilistic Prediction
- machine-learning
- gradient-boosting
- natural-gradients
- uncertainty-estimation
47,133 repositories indexed
49–69 of 69 · Most stars
Natural Gradient Boosting for Probabilistic Prediction
Synthetic data generators for tabular and time-series data
Koç University deep learning framework.
Instructional notebooks on music information retrieval.
TorchXRayVision: A library of chest X-ray datasets and models. Classifiers, segmentation, and autoencoders.
This repository introduces PIXIU, an open-source resource featuring the first financial large language models (LLMs), instruction tuning data, and evaluation benchmarks to holistically assess financial LLMs. Our goal is to continually push forward the open-source development of financial artificial intelligence (AI).
Precision Medicine Knowledge Graph (PrimeKG)
🍇 GRAPE is a Rust/Python Graph Representation Learning library for Predictions and Evaluations
food image to recipe with deep convolutional neural networks.
Slides, scripts and materials for the Machine Learning in Finance Course at NYU Tandon, 2022
Cleora AI is a general-purpose open-source model for efficient, scalable learning of stable and inductive entity embeddings for heterogeneous relational data. Created by Synerise.com team.
Python package for Bayesian Machine Learning with scikit-learn API
Sources codes for: Mastering Python for Finance, Second Edition
An OCaml kernel for Jupyter (IPython) notebook
Materials for GWU DNSC 6279 and DNSC 6290.
Usiigaci: stain-free cell tracking in phase contrast microscopy enabled by supervised machine learning
ChatGPT Python Applications integrated with third party libraries and modules
⬛ Python Individual Conditional Expectation Plot Toolbox
Optimising chemical reactions using machine learning
Peax is a tool for interactive visual pattern search and exploration in epigenomic data based on unsupervised representation learning with autoencoders
A coding exercise: let's convert dirty machine learning code into clean code using a Pipeline - which is the Pipe and Filter Design Pattern applied to Machine Learning.