gds_course VS gds_env

Compare gds_course vs gds_env and see what are their differences.

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gds_course gds_env
1 1
86 125
- -
3.5 7.8
5 months ago 12 days ago
Jupyter Notebook Jupyter Notebook
- BSD 3-clause "New" or "Revised" License
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.
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.

gds_course

Posts with mentions or reviews of gds_course. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-09-18.

gds_env

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

What are some alternatives?

When comparing gds_course and gds_env you can also consider the following projects:

chrisalbon_com - Code for ChrisAlbon.com

AMAYARA-Lab - The γ‚’γƒžγƒ€γƒ© Lab project provides a ready-to-use Jupyter Lab environment to help out with Android malware analysis using YARA rules.

ML-Workspace - πŸ›  All-in-one web-based IDE specialized for machine learning and data science.

FinMind - Open Data, more than 50 financial data. ζδΎ›θΆ…ιŽ 50 ε€‹ι‡‘θžθ³‡ζ–™(台肑為主)οΌŒζ―ε€©ζ›΄ζ–° https://finmind.github.io/

fastpages - An easy to use blogging platform, with enhanced support for Jupyter Notebooks.

code - Compilation of R and Python programming codes on the Data Professor YouTube channel.

reinforcement_learning_course_materials - Lecture notes, tutorial tasks including solutions as well as online videos for the reinforcement learning course hosted by Paderborn University

H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.