NyuziProcessor
catboost
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NyuziProcessor | catboost | |
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10 | 8 | |
1,916 | 7,744 | |
- | 1.6% | |
3.4 | 9.9 | |
1 day ago | 2 days ago | |
C | Python | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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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.
NyuziProcessor
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Tiny GPU: A minimal GPU implementation in Verilog
Nice! I warmly encourage open-core GPU work.
Here's another: https://github.com/jbush001/NyuziProcessor
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FuryGpu – Custom PCIe FPGA GPU
There's also Nyuzi which is more GPGPU focused https://github.com/jbush001/NyuziProcessor, but the author also experimented with having it do 3D graphics.
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The WeeCee – Tiny Vortex86-Based DOS Gaming PC
You could probably layer a software implementation of the rasterization pipeline on top of a compute-focused open-source GPU architecture like Nyuzi: https://github.com/jbush001/NyuziProcessor/
I would expect a 2x slowdown over hardware rasterization, based on NVIDIA's work on such an approach, but this is probably fine if you're just trying to match Voodoo3 performance. And one could imagine bolting a minimal hardware rasterizer on top of Nyuzi to speed things up once the software implementation is working.
- FPGA as a GPU for Linux
- Nyuzi – An Experimental Open-Source FPGA GPGPU Processor
- An Experimental (Open-Source FPGA) GPGPU Processor called Nyuzi (by @jbush001)
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Exploring RustFFT's SIMD Architecture
Sounds like the Nyuzi processor :D
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?
vdpau-va-driver-vp9 - Experimental VP9 codec support for vdpau-va-driver (NVIDIA VDPAU-VAAPI wrapper) and chromium-vaapi
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
openvga
Recommender - A C library for product recommendations/suggestions using collaborative filtering (CF)
TrellisBoard - Ultimate ECP5 development board
Keras - Deep Learning for humans
hardware - Verilog development and verification project for HOL4
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
ao486_MiSTer - ao486 port for MiSTer
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.
OpenSimplex2 - C implementation for CPU and GPU of OpenSimplex 2
mxnet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more