GPU-Raytracer
catboost
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| GPU-Raytracer | catboost | |
|---|---|---|
| 28 | 8 | |
| 761 | 7,744 | |
| - | 1.6% | |
| 3.6 | 9.9 | |
| almost 2 years ago | 3 days ago | |
| C++ | Python | |
| MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
GPU-Raytracer
- What is it about my pathtracer that makes it look not very good?(more details in comment)
- C++/CUDA Raytracer from Scratch
- C++/CUDA Pathtracer from Scratch
- C++/CUDA Physically Based GPU Pathtracer from Scratch
- GPU raytracing engine from scratch
- Highly Optimized Realistic GPU Raytracer
- CUDA Pathtracer
- Hobby project: C++/CUDA Pathtracer
- C++ Show and Tell - April 2022
catboost
- CatBoost: Open-source gradient boosting library
- Boosting Algorithms
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What's New with AWS: Amazon SageMaker built-in algorithms now provides four new Tabular Data Modeling Algorithms
CatBoost is another popular and high-performance open-source implementation of the Gradient Boosting Decision Tree (GBDT). To learn how to use this algorithm, please see example notebooks for Classification and Regression.
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Writing the fastest GBDT libary in Rust
Here are our benchmarks on training time comparing Tangram's Gradient Boosted Decision Tree Library to LightGBM, XGBoost, CatBoost, and sklearn.
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Data Science toolset summary from 2021
Catboost - CatBoost is an open-source software library developed by Yandex. It provides a gradient boosting framework which attempts to solve for Categorical features using a permutation driven alternative compared to the classical algorithm. Link - https://catboost.ai/
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CatBoost Quickstart — ML Classification
CatBoost is an open source algorithm based on gradient boosted decision trees. It supports numerical, categorical and text features. Check out the docs.
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[D] What are your favorite Random Forest implementations that support categoricals
If you considering GBDT check out catboost, unfortunately RF mode is not available but library implement lots of interesting categorical encoding tricks that boost accuracy.
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CatBoost and Water Pumps
The data contains a large number of categorical features. The most suitable for obtaining a base-line model, in my opinion, is CatBoost. It is a high-performance, open-source library for gradient boosting on decision trees.
What are some alternatives?
GLSL-PathTracer - A toy physically based GPU path tracer (C++/OpenGL/GLSL)
xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
seer - Seer - a gui frontend to gdb
Recommender - A C library for product recommendations/suggestions using collaborative filtering (CF)
Open3D - Open3D: A Modern Library for 3D Data Processing
Keras - Deep Learning for humans
ROCm-OpenCL-Runtime - ROCm OpenOpenCL Runtime
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
libriscv - C++20 RISC-V RV32/64/128 userspace emulator library
vowpal_wabbit - Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.
Monocle
mxnet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more