deepirtools
Deep learning-based estimation and inference for item response theory models. (by cjurban)
Pytorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration (by pytorch)
| deepirtools | Pytorch | |
|---|---|---|
| 1 | 341 | |
| 17 | 78,642 | |
| - | 2.2% | |
| 10.0 | 10.0 | |
| over 1 year ago | 2 days ago | |
| Python | Python | |
| GNU General Public License v3.0 only | BSD 1-Clause 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.
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.
deepirtools
Posts with mentions or reviews of deepirtools.
We have used some of these posts to build our list of alternatives
and similar projects.
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[Q] PCA on an all-binary dataset?
Agreed, item response theory (IRT) would be a principled approach to use. I recently released a Python package called DeepIRTools that could be helpful here. It uses a deep learning approach to fit IRT models and provides a method for determining the latent dimensionality (i.e., how many "components" to retain). To get the dimension-reduced data (called embeddings in deep learning, factor scores in IRT, and components in PCA), you would just call model.scores(data).
Pytorch
Posts with mentions or reviews of Pytorch.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-05-01.
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Clasificador de imágenes con una red neuronal convolucional (CNN)
PyTorch (https://pytorch.org/)
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AI enthusiasm #9 - A multilingual chatbot📣🈸
torch is a package to manage tensors and dynamic neural networks in python (GitHub)
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Einsum in 40 Lines of Python
PyTorch also has some support for them, but it's quite incomplete and has many issues so that it is basically unusable. And its future development is also unclear. https://github.com/pytorch/pytorch/issues/60832
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Library for Machine learning and quantum computing
TensorFlow
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My Favorite DevTools to Build AI/ML Applications!
TensorFlow, developed by Google, and PyTorch, developed by Facebook, are two of the most popular frameworks for building and training complex machine learning models. TensorFlow is known for its flexibility and robust scalability, making it suitable for both research prototypes and production deployments. PyTorch is praised for its ease of use, simplicity, and dynamic computational graph that allows for more intuitive coding of complex AI models. Both frameworks support a wide range of AI models, from simple linear regression to complex deep neural networks.
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penzai: JAX research toolkit for building, editing, and visualizing neural nets
> does PyTorch have a similar concept
of course https://github.com/pytorch/pytorch/blob/main/torch/utils/_py...
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Tinygrad: Hacked 4090 driver to enable P2P
fyi should work on most 40xx[1]
[1] https://github.com/pytorch/pytorch/issues/119638#issuecommen...
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The Elements of Differentiable Programming
Sure, right here: https://github.com/pytorch/pytorch/blob/main/torch/autograd/...
Here's the documentation: https://pytorch.org/tutorials/intermediate/forward_ad_usage....
> When an input, which we call “primal”, is associated with a “direction” tensor, which we call “tangent”, the resultant new tensor object is called a “dual tensor” for its connection to dual numbers[0].
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Functions and operators for Dot and Matrix multiplication and Element-wise calculation in PyTorch
*My post explains Dot, Matrix and Element-wise multiplication in PyTorch.
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Dot vs Matrix vs Element-wise multiplication in PyTorch
In PyTorch with @, dot() or matmul():