xgboost_ray VS d2l-en

Compare xgboost_ray vs d2l-en and see what are their differences.

xgboost_ray

Distributed XGBoost on Ray (by ray-project)

d2l-en

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge. (by d2l-ai)
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xgboost_ray d2l-en
1 6
116 19,183
5.2% 3.1%
6.0 9.3
7 days ago 24 days ago
Python Python
Apache License 2.0 GNU General Public License v3.0 or later
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.

xgboost_ray

Posts with mentions or reviews of xgboost_ray. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning xgboost_ray yet.
Tracking mentions began in Dec 2020.

d2l-en

Posts with mentions or reviews of d2l-en. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-04-10.

What are some alternatives?

When comparing xgboost_ray and d2l-en you can also consider the following projects:

Pytorch-UNet - PyTorch implementation of the U-Net for image semantic segmentation with high quality images

DeepADoTS - Repository of the paper "A Systematic Evaluation of Deep Anomaly Detection Methods for Time Series".

imbalanced-regression - [ICML 2021, Long Talk] Delving into Deep Imbalanced Regression

TF-Watcher - Monitor your ML jobs on mobile devices­čô▒, especially for Google Colab / Kaggle

99-ML-Learning-Projects - A list of 99 machine learning projects for anyone interested to learn from coding and building projects

ssd_keras - A Keras port of Single Shot MultiBox Detector

petastorm - Petastorm library enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format. It supports ML frameworks such as Tensorflow, Pytorch, and PySpark and can be used from pure Python code.

learning-topology-synthetic-data - Tensorflow implementation of Learning Topology from Synthetic Data for Unsupervised Depth Completion (RAL 2021 & ICRA 2021)

einops - Deep learning operations reinvented (for pytorch, tensorflow, jax and others)

mljar-supervised - Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

ScanRefer - [ECCV 2020] ScanRefer: 3D Object Localization in RGB-D Scans using Natural Language

textgenrnn - Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code.