bitcoin_price_prediction VS Anomaly_Detection_Tuto

Compare bitcoin_price_prediction vs Anomaly_Detection_Tuto and see what are their differences.

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bitcoin_price_prediction Anomaly_Detection_Tuto
1 2
10 182
- -
0.0 0.0
about 3 years ago about 3 years ago
Jupyter Notebook 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.

bitcoin_price_prediction

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

Anomaly_Detection_Tuto

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

What are some alternatives?

When comparing bitcoin_price_prediction and Anomaly_Detection_Tuto you can also consider the following projects:

mlnotify - 🔔 No need to keep checking your training - just one import line and you'll know the second it's done.

alibi-detect - Algorithms for outlier, adversarial and drift detection

NYU-DLSP20 - NYU Deep Learning Spring 2020

tsfresh - Automatic extraction of relevant features from time series:

TrainInvaders - 👾 Jupyter Notebook + Space Invaders!?

mix_merkletree - Usefull Merkle Tree Example

it-salary-analysis - 💰 Analysis of Salaries in IT Roles: DevOps, Cyber Security, and AI

Python-Bible - Resourceful Python Collection

Animender - An AI that recommends anime based on personal history.

biggestwar_ai

peacasso - UI interface for experimenting with multimodal (text, image) models (stable diffusion).

diversity_measures - Code for the paper: Diversity Measures: Domain Independent Proxies for Failure in Language Model Queries