compression VS deephyper

Compare compression vs deephyper and see what are their differences.

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compression deephyper
1 1
823 261
2.6% 2.7%
6.6 7.9
10 days ago 17 days ago
Python Python
Apache License 2.0 BSD 3-clause "New" or "Revised" 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.

compression

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

deephyper

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

What are some alternatives?

When comparing compression and deephyper you can also consider the following projects:

EmoPy - A deep neural net toolkit for emotion analysis via Facial Expression Recognition (FER)

autokeras - AutoML library for deep learning

openrec - OpenRec is an open-source and modular library for neural network-inspired recommendation algorithms

wandb - 🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.

serving - A flexible, high-performance serving system for machine learning models

archai - Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.

guesslang - Detect the programming language of a source code

keras-tuner - A Hyperparameter Tuning Library for Keras

tensorflow - An Open Source Machine Learning Framework for Everyone

Note - Easily implement parallel training and distributed training. Machine learning library. Note.neuralnetwork.tf package include Llama2, Llama3, CLIP, ViT, ConvNeXt, SwiftFormer, etc, these models built with Note are compatible with TensorFlow and can be trained with TensorFlow.

ml-engineering - Machine Learning Engineering Open Book