engineering-management VS SUBLIME

Compare engineering-management vs SUBLIME and see what are their differences.

engineering-management

A collection of inspiring resources related to engineering management and tech leadership (by charlax)

SUBLIME

Semantically Understanding Bias with LIME. Using the LIME-RS Algorithm to understand bias in recommender systems. (by FurkanToprak)
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engineering-management SUBLIME
9 1
7,189 3
- -
6.7 10.0
7 days ago over 2 years ago
Shell Jupyter Notebook
MIT 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.

engineering-management

Posts with mentions or reviews of engineering-management. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-06-19.

SUBLIME

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

What are some alternatives?

When comparing engineering-management and SUBLIME you can also consider the following projects:

the-engineering-managers-booklist - Books for people who are or aspire to manage/lead team(s) of software engineers

qt-dab - Qt-DAB, a general software DAB (DAB+) decoder with a (slight) focus on showing the signal

awesome-leading-and-managing - Awesome List of resources on leading people and being a manager. Geared toward tech, but potentially useful to anyone.

interpret - Fit interpretable models. Explain blackbox machine learning.

GameDevMind - 最全面的游戏开发技术图谱。帮助游戏开发者们在已知问题上节省时间,省出更多的精力投入到更有创造性的工作中去。

shapash - 🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

AIF360 - A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

CWOX - A XAI Framework to provide Contrastive Whole-output Explanation for Image Classification.

image-crop-analysis - Code for reproducing our analysis in the paper titled: Image Cropping on Twitter: Fairness Metrics, their Limitations, and the Importance of Representation, Design, and Agency