deep-significance VS openrec

Compare deep-significance vs openrec and see what are their differences.

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deep-significance openrec
6 1
316 406
- -
4.0 0.0
7 months ago about 1 year ago
Python Python
GNU General Public License v3.0 only Apache License 2.0
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.

deep-significance

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

openrec

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

What are some alternatives?

When comparing deep-significance and openrec you can also consider the following projects:

nannyml - nannyml: post-deployment data science in python

TensorRec - A TensorFlow recommendation algorithm and framework in Python.

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.

compression - Data compression in TensorFlow

ludwig - Low-code framework for building custom LLMs, neural networks, and other AI models

recommenders - Best Practices on Recommendation Systems

horovod - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. [Moved to: https://github.com/horovod/horovod]

implicit - Fast Python Collaborative Filtering for Implicit Feedback Datasets

horovod - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.

deephyper - DeepHyper: Scalable Asynchronous Neural Architecture and Hyperparameter Search for Deep Neural Networks

clearml - ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution

LargeBatchCTR - Large batch training of CTR models based on DeepCTR with CowClip.