hyper-nn VS nn

Compare hyper-nn vs nn and see what are their differences.

nn

🧑‍🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠 (by lab-ml)
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hyper-nn nn
1 26
91 48,430
- 4.5%
0.0 7.7
over 1 year ago about 1 month ago
Jupyter Notebook Jupyter Notebook
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.

hyper-nn

Posts with mentions or reviews of hyper-nn. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-08-11.

nn

Posts with mentions or reviews of nn. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-01-09.

What are some alternatives?

When comparing hyper-nn and nn you can also consider the following projects:

neural-tangents - Fast and Easy Infinite Neural Networks in Python

GFPGAN-for-Video-SR - A colab notebook for video super resolution using GFPGAN

uvadlc_notebooks - Repository of Jupyter notebook tutorials for teaching the Deep Learning Course at the University of Amsterdam (MSc AI), Fall 2023

labml - 🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱

functorch - functorch is JAX-like composable function transforms for PyTorch.

ZoeDepth - Metric depth estimation from a single image

onnx-simplifier - Simplify your onnx model

Basic-UI-for-GPT-J-6B-with-low-vram - A repository to run gpt-j-6b on low vram machines (4.2 gb minimum vram for 2000 token context, 3.5 gb for 1000 token context). Model loading takes 12gb free ram.

Behavior-Sequence-Transformer-Pytorch - This is a pytorch implementation for the BST model from Alibaba https://arxiv.org/pdf/1905.06874.pdf

DFL-Colab - DeepFaceLab fork which provides IPython Notebook to use DFL with Google Colab

Siren-fastai2 - Unofficial implementation of 'Implicit Neural Representations with Periodic Activation Functions'

vision - Datasets, Transforms and Models specific to Computer Vision