templateGen
Souce code generator from a given template (by eliaskanelis)
ivy
The Unified Machine Learning Framework [Moved to: https://github.com/unifyai/ivy] (by ivy-dl)
templateGen | ivy | |
---|---|---|
1 | 1 | |
3 | 10,475 | |
- | - | |
3.0 | 10.0 | |
10 days ago | about 1 year ago | |
Python | Python | |
GNU General Public License v3.0 only | GNU General Public License v3.0 or later |
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.
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.
templateGen
Posts with mentions or reviews of templateGen.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-04-15.
ivy
Posts with mentions or reviews of ivy.
We have used some of these posts to build our list of alternatives
and similar projects.
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Need internship/part time role in AI/ML/Data Science field.
As a volunteer developer at Ivy, I also been actively contributing to the project for several months now. I extended the Ivy Functional API by adding some linear algebra functions - matrix_exp (https://github.com/ivy-dl/ivy/blob/dev/ivy/linalg/matrix_exp.py) and diff (https://github.com/ivy-dl/ivy/blob/dev/ivy/linalg/diff.py) - to all four popular deep learning frameworks: NumPy, TensorFlow, PyTorch, and JAX. These functions allowed developers to perform matrix exponentiation and differentiation operations more efficiently and easily using any of these frameworks.