mlcourse.ai
attractors
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mlcourse.ai | attractors | |
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85 | 3 | |
9,390 | 45 | |
- | - | |
3.4 | 2.9 | |
4 months ago | almost 2 years ago | |
Python | Python | |
GNU General Public License v3.0 or later | MIT License |
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.
mlcourse.ai
attractors
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Strange Attractors - Aizawa [3840x2160] Link for more in comments + Source code
All images (without any text) - link
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Strange Attractors (My first package on pip)
Oh, sorry! Seems like you're trying to run the package in python 3.7. The package currently only supports Python 3.8 and above (3.7 and below may have some bugs in matplotlib). If you want, you can install from source (github)
What are some alternatives?
napari - napari: a fast, interactive, multi-dimensional image viewer for python
cheatsheets - Official Matplotlib cheat sheets
concrete-numpy - Concrete-Numpy: A library to turn programs into their homomorphic equivalent.
integrals - Computing integrals
GreyNSights - Privacy-Preserving Data Analysis using Pandas
strange-attractor-renderer - A multithreaded strange attractor renderer
hiitpi - A workout trainer Dash/Flask app that helps track your HIIT workouts by analyzing real-time video streaming from your sweet Pi using machine learning and Edge TPU..
quaternion - Add built-in support for quaternions to numpy
Grafana - The open and composable observability and data visualization platform. Visualize metrics, logs, and traces from multiple sources like Prometheus, Loki, Elasticsearch, InfluxDB, Postgres and many more.
WaveNCC - An app to compute the normalization coefficients of a given set of orthogonal 1D complex wave functions.
data-science-ipython-notebooks - Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
julia - The Julia Programming Language