Natural Gradient Boosting for Probabilistic Prediction
- machine-learning
- gradient-boosting
- natural-gradients
- uncertainty-estimation
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Natural Gradient Boosting for Probabilistic Prediction
Gallery of OSMnx tutorials, usage examples, and feature demonstrations.
PyTorch implementation of various methods for continual learning (XdG, EWC, SI, LwF, FROMP, DGR, BI-R, ER, A-GEM, iCaRL, Generative Classifier) in three different scenarios.
A curated list of awesome embedding models tutorials, projects and communities.
1st place solution
The repository host gives no description.
For extensive instructor led learning
Practical course about Large Language Models.
WFGY is heading toward WFGY 5.0 Polaris Protocol, a major open-source release for AI reasoning, RAG, agents, and real-world workflows. Includes Problem Map, Global Debug Card, WFGY 4.0, and the CFV Easter Egg.
Discovering Interpretable GAN Controls [NeurIPS 2020]
Deep Learning and Machine Learning stocks represent promising opportunities for both long-term and short-term investors and traders.
Temporal Graph Convolutional Network for Urban Traffic Flow Prediction Method
Awesome-pytorch-list 翻译工作进行中......
DL course co-developed by YSDA, HSE and Skoltech
This course is designed to guide beginners through the exciting world of Edge AI, covering fundamental concepts, popular models, inference techniques, device-specific applications, model optimization, and the development of intelligent Edge AI agents.
Try out deep learning models online on Google Colab
Notes on the Deep Learning book from Ian Goodfellow, Yoshua Bengio and Aaron Courville (2016)
A Modular Framework for 3D Gaussian Splatting and Beyond
fastquant — Backtest and optimize your ML trading strategies with only 3 lines of code!
Implementation of XFeat (CVPR 2024). Do you need robust and fast local feature extraction? You are in the right place!
Deep Learning Specialization by Andrew Ng, deeplearning.ai.
Implementing the 4 agentic patterns from scratch
Complex code hides connections, workflows, and business risks.Unvibe complex code. Trace business workflows.
《机器学习理论导引》(宝箱书)的证明、案例、概念补充与参考文献讲解。