Intrusion-Detection-System-Using-Machine-Learning VS walk_in_the_park

Compare Intrusion-Detection-System-Using-Machine-Learning vs walk_in_the_park and see what are their differences.

Our great sponsors
  • InfluxDB - Power Real-Time Data Analytics at Scale
  • WorkOS - The modern identity platform for B2B SaaS
  • SaaSHub - Software Alternatives and Reviews
Intrusion-Detection-System-Using-Machine-Learning walk_in_the_park
3 2
320 226
6.9% -
2.9 0.0
7 months ago over 1 year ago
Jupyter Notebook Python
MIT License 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.

Intrusion-Detection-System-Using-Machine-Learning

Posts with mentions or reviews of Intrusion-Detection-System-Using-Machine-Learning. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-09-03.

walk_in_the_park

Posts with mentions or reviews of walk_in_the_park. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-09-03.

What are some alternatives?

When comparing Intrusion-Detection-System-Using-Machine-Learning and walk_in_the_park you can also consider the following projects:

VideoX - VideoX: a collection of video cross-modal models

textual_inversion

MinVIS

Cold-Diffusion-Models - Official implementation of Cold-Diffusion for different transformations in pytorch.

Awesome-Dataset-Distillation - Awesome Dataset Distillation Papers

bitsandbytes - Accessible large language models via k-bit quantization for PyTorch.

PeRFception - [NeurIPS2022] Official implementation of PeRFception: Perception using Radiance Fields.