deep-finance VS DataAug4Code

Compare deep-finance vs DataAug4Code and see what are their differences.

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deep-finance DataAug4Code
1 3
1,135 49
- -
10.0 6.6
almost 2 years ago about 2 months ago
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.

deep-finance

Posts with mentions or reviews of deep-finance. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-03-19.

DataAug4Code

Posts with mentions or reviews of DataAug4Code. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-11-16.

What are some alternatives?

When comparing deep-finance and DataAug4Code you can also consider the following projects:

financial-machine-learning - A curated list of practical financial machine learning tools and applications.

time-series-transformers-review - A professionally curated list of awesome resources (paper, code, data, etc.) on transformers in time series.

GNNPapers - Must-read papers on graph neural networks (GNN)

DataAug4NLP - Collection of papers and resources for data augmentation for NLP.

awesome-stellar - 👨‍🚀 A curated list of Stellar applications, blog posts, educational resources, tools, and more.

yt-channels-DS-AI-ML-CS - A comprehensive list of 180+ YouTube Channels for Data Science, Data Engineering, Machine Learning, Deep learning, Computer Science, programming, software engineering, etc.

internet-explorer - Internet Explorer explores the web in a self-supervised manner to progressively find relevant examples that improve performance on a desired target dataset.

awesome-refreshing-llms - EMNLP'23 survey: a curation of awesome papers and resources on refreshing large language models (LLMs) without expensive retraining.