catboost
Dlib
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catboost | Dlib | |
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8 | 33 | |
7,731 | 12,991 | |
1.4% | - | |
9.9 | 7.9 | |
1 day ago | 8 days ago | |
Python | C++ | |
Apache License 2.0 | Boost Software License 1.0 |
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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.
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.
Dlib
- Modern Image Processing Algorithms Implementation in C
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[Cpp] Une assez grande liste de bibliothèques graphiques C ++
dlib
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32 years old. HRT in April or May. Things I can do to maximize results and what to expect.
The apparent gender estimates from photos are using dlib, and I really ought to get what I'm doing cleaned up in such a way that other people can use it easily.
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What are some C++ projects with high quality code that I can read through?
I really like dlib's code https://github.com/davisking/dlib
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C++ for machine learning
Additionally, C++ may be used for extremely high levels of optimization even for cloud-based ML. Dlib and Kaldi are C++ libraries used as dependencies in Python codebases for computer vision and audio processing, for example. So if your application requires you to customize any functions similar to those libraries, then you'll need C++ knowhow.
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What programming language should I learn after C++ for Audio DSP?
If you know C++, you don't need anything else. Go and learn APIs for C++ libraries. If you're into DSP, why not study Dlib?.
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Exponential vs linear progress?
The data is mostly in this spreadsheet. The apparently facial gender estimates are made with Dlib. The mental health assessments are from Beck's Depression Inventory and the Snaith-Hamilton Pleasure Scale. The graph is made with gnuplot.
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Flutter OpenCV and dlib for face detector & recognition
The plugin uses dlib library with a very fast HOG detector for both face recognition and detector following the relative examples.
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How long after starting HRT did facial recognition not recognize you?
The dlib facial recognition model thinks that I am now a distance of about 0.3 from where I started, which is far enough to start getting many false positive matches, but still within the design intent that different pictures of the same individual will be within 0.6 of each other.
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Does hrt effect facial recognition software?
Dlib's face recognition module thinks that I am about 0.25 units away from where I started; its design intent is that distinct individuals will be 0.6 or more apart, although in practice other people start showing up around 0.3.
What are some alternatives?
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
mlpack - mlpack: a fast, header-only C++ machine learning library
Recommender - A C library for product recommendations/suggestions using collaborative filtering (CF)
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
Keras - Deep Learning for humans
Boost - Super-project for modularized Boost
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
Face Recognition - The world's simplest facial recognition api for Python and the command line
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.
OpenCV - Open Source Computer Vision Library
mxnet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
Caffe - Caffe: a fast open framework for deep learning.