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MXNet VS CatBoost

Compare MXNet VS CatBoost and see what are their differences

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  2. » Videos
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MXNet logo MXNet

MXNet is a deep learning framework.

CatBoost logo CatBoost

CatBoost - state-of-the-art open-source gradient boosting library with categorical features support, https://catboost.yandex/ #catboost
  • MXNet Landing page
    Landing page //
    2022-07-25
  • CatBoost Landing page
    Landing page //
    2021-10-16

MXNet videos

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Apache MXNet 2.0: Bridging Deep Learning and Machine Learning

More videos:

  • Review - MXNet Introduction: MXNet Vancouver Meetup
  • Review - Extending Apache MXNet for new features and performance

CatBoost videos

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[Paper Review]Catboost: Unbiased Boosting with Categorical Features

More videos:

  • Review - 04-9: Ensemble Learning - CatBoost (앙상블 기법 - CatBoost)
  • Review - Free Udemy Course - CatBoost vs XGBoost - Classification and Regression Modeling with Python

Category Popularity

0-100% (relative to MXNet and CatBoost)

MXNet

CatBoost

Data Science And Machine Learning
55 55%
Data Science And Machine Learning
45% 45
Machine Learning
46 46%
Machine Learning
54% 54
Data Science Tools
37 37%
Data Science Tools
63% 63
Business & Commerce
100 100%
Business & Commerce
0% 0

User comments

Share your experience with using MXNet and CatBoost. For example, how are they different and which one is better?
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Social recommendations and mentions

Based on our record, CatBoost seems to be more popular. It has been mentiond 4 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

MXNet mentions (0)

We have not tracked any mentions of MXNet yet. Tracking of MXNet recommendations started around Mar 2021.

CatBoost mentions (4)

  • 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. - Source: dev.to / almost 2 years ago
  • 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. - Source: dev.to / over 2 years ago
  • 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/. - Source: dev.to / over 2 years ago
  • 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. - Source: dev.to / about 3 years ago

What are some alternatives?

When comparing MXNet and CatBoost, you can also consider the following products

TensorFlow - TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.

Open Text Magellan - OpenText Magellan - the power of AI in a pre-wired platform that augments decision making and accelerates your business. Learn more.

PyTorch - Open source deep learning platform that provides a seamless path from research prototyping to...

Kira - Gain visibility into contract repositories, accelerate and improve the accuracy of contract review, mitigate risk of errors, win new business, and improve the value you provide to your clients.

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

BAAR - BAAR is a Business Workflow Automation platform to help you automate digital security.

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