rikai
Parquet-based ML data format optimized for working with unstructured data (by eto-ai)
book
PDFs and Codelabs for the Efficient Deep Learning book. (by EfficientDL)
Our great sponsors
| rikai | book | |
|---|---|---|
| 1 | 2 | |
| 135 | 183 | |
| 0.7% | 0.5% | |
| 0.0 | 2.7 | |
| over 1 year ago | 11 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| 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.
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.
rikai
Posts with mentions or reviews of rikai.
We have used some of these posts to build our list of alternatives
and similar projects.
-
[P] Active Learning Simplified with Rikai
We (Eto.ai) are building an open-source unstructured ML data (image, video, sensor) framework, called Rikai, which features:
book
Posts with mentions or reviews of book.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-08-01.
-
[P] Using Sparsity & Clustering to compress your models: Efficient Deep Learning Book
We now have a new chapter focusing on sparsity and clustering, two advanced compression techniques that you can use to reduce the footprint of your model (size, latency, etc.) while retaining your model accuracy. You can read the chapter here, and go through the accompanying codelabs here.
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[P] Efficient Deep Learning Book
The goal is to introduce these ideas in a single place, without having to parse many papers, try to get a working code sample, and then spend time debugging. With the accompanying codelabs, we hope that our readers can make their models 4-20x smaller, faster, and better in quality.
What are some alternatives?
When comparing rikai and book you can also consider the following projects:
DeepLearningExamples - State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.