hyperparameter
Hyperparameter, Make configurable AI applications.Build for Python hackers. (by reiase)
Keras
Deep Learning for humans (by keras-team)
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hyperparameter | Keras | |
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7 | 76 | |
23 | 60,902 | |
- | 0.6% | |
7.1 | 9.9 | |
30 days ago | 5 days ago | |
Rust | Python | |
Apache License 2.0 | 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.
hyperparameter
Posts with mentions or reviews of hyperparameter.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-04-10.
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Hyper-parameter Optimization with Optuna and hyperparameter
the full tutorial: https://github.com/reiase/hyperparameter/tree/master/examples/optuna
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Pythonic configuration framework?
When I was working on my own configuration framework (HyperParameter, previous post), I suddenly realize that what I want is not another configuration framework with some fancy API. All I want is to change my ML experiments without modifying the code and get rid of the configuration handling codes. The right way of configuration is not writing configurable code and wasting time on different frameworks. The best solution is a tool that makes your code configurable.
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hyperparameter, a lightweight configuration framework
github: https://github.com/reiase/hyperparameter
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HyperParameter for ML Models and Systems
HyperParameter is a configuration and parameter management library for Python. HyperParameter provides the following features:
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What is the best practice for injecting configuration into a python application
you can take a look at https://github.com/reiase/hyperparameter, a scoped, thread-safe config object that is lightweight enough. There is no need to modify too much code:
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[P] Modify Hyperparameters Easily
I'm developing a Hyperparameter tuning toolbox for my machine learning projects. It maps keyword arguments to hyper-parameters, for example:
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A hyper-parameter toolbox for data-scientists and machine-learning engineers
I'm developing [a toolbox for managing hyper-parameters](https://github.com/reiase/hyperparameter) in my data science and machine learning projects. It provides object-style API for nested dict( which is very common for config files):
Keras
Posts with mentions or reviews of Keras.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-04-15.
- 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.
- free categorical predictive analytic software?
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I got advice on building ai apps.
Keras documentation: https://keras.io/