24pullrequests
Keras
| 24pullrequests | Keras | |
|---|---|---|
| 8 | 78 | |
| 1,648 | 60,972 | |
| 0.2% | 0.3% | |
| 9.4 | 9.9 | |
| 9 days ago | 1 day ago | |
| Ruby | Python | |
| MIT License | Apache License 2.0 |
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.
24pullrequests
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Perl support in Liquidprompt
Now that I decided to join the 24 PRs, I decided to give it another go.
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Why is contributing soo hard
Further reading: - Revitalizing stalled open source projects - 5 Ways to Get Started in Open Source - How to contribute to open source - 24pullrequests.com
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A Beginner’s Guide to Open-Source Contribution for Developers
Other platforms include Good First Issues, 24 Pull Requests and Code Triage.
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2022 Advent Code Challenges
A project that encourages developers to send a PR to an open source project every day for 24 days. They have many featured projects. 24 pull requests
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How to find open source projects to contribute
The way I got started with open source was via an initiative to incentivize people to contribute during December, 24 Pull Requests. I decided to make a first small contribution using Markdown, which you can check out at FrancesCoronel/hire-me/pull/9 on GitHub.
- 分享几个自学编程/寻找开源项目练手/免费寻找mentor的网站
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Level up your Python today with open-source contributions
24 Pull Requests
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Have fun, and contribute to Open Source, with 24 Pull Requests! 🎁
If you're reading this on DEV, the chances are that you're already familiar with how to contribute to an open source project. If not, there are a bunch of articles and resources collected together on the web page. You can also ask questions via GitHub Discussions or on Gitter, if you're not sure about something.
Keras
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Library for Machine learning and quantum computing
Keras
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My Favorite DevTools to Build AI/ML Applications!
As a beginner, I was looking for something simple and flexible for developing deep learning models and that is when I found Keras. Many AI/ML professionals appreciate Keras for its simplicity and efficiency in prototyping and developing deep learning models, making it a preferred choice, especially for beginners and for projects requiring rapid development.
- Release: Keras 3.3.0
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Getting Started with Gemma Models
After setting the variables for the environment, the next step is to install dependencies. To use Gemma, KerasNLP is the dependency used. KerasNLP is a collection of natural language processing (NLP) models implemented in Keras and runnable on JAX, PyTorch, and TensorFlow.
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Keras 3.0
All breaking changes are listed here: https://github.com/keras-team/keras/issues/18467
You can use this migration guide to identify and fix each of these issues (and further, making your code run on JAX or PyTorch): https://keras.io/guides/migrating_to_keras_3/
- Keras 3: A new multi-back end Keras
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Can someone explain how keras code gets into the Tensorflow package?
I'm guessing the "real" keras code is coming from the keras repository. Is that a correct assumption? How does that version of Keras get there? If I wanted to write my own activation layer next to ELU, where exactly would I do that?
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How popular are libraries in each technology
Other popular machine learning tools include PyTorch, Keras, and Scikit-learn. PyTorch is an open-source machine learning library developed by Facebook that is known for its ease of use and flexibility. Keras is a high-level neural networks API that is written in Python and is known for its simplicity. Scikit-learn is a machine learning library for Python that is used for data analysis and data mining tasks.
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List of AI-Models
Click to Learn more...
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Official Question Thread! Ask /r/photography anything you want to know about photography or cameras! Don't be shy! Newbies welcome!
I'm not aware of anything off-the-shelf, but if you have sufficient programming experience, one way to do this would be to build a large dataset of reference images and pictures and use something like keras to train a convolutional neural network on them.
What are some alternatives?
WebsiteOne - A website for Agile Ventures
MLP Classifier - A handwritten multilayer perceptron classifer using numpy.
explore - Community-curated topic and collection pages on GitHub
scikit-learn - scikit-learn: machine learning in Python
CodeTriage - Discover the best way to get started contributing to Open Source projects
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
tech404logs - Free archives for the Tech404 Slack
xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
Ansible - Ansible is a radically simple IT automation platform that makes your applications and systems easier to deploy and maintain. Automate everything from code deployment to network configuration to cloud management, in a language that approaches plain English, using SSH, with no agents to install on remote systems. https://docs.ansible.com.
tensorflow - An Open Source Machine Learning Framework for Everyone
Zulip - Zulip server and web application. Open-source team chat that helps teams stay productive and focused.
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.