QuickQanava
Keras
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QuickQanava | Keras | |
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4 | 77 | |
1,074 | 60,937 | |
- | 0.6% | |
8.9 | 9.9 | |
25 days ago | about 8 hours ago | |
C++ | Python | |
GNU General Public License v3.0 or later | Apache License 2.0 |
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QuickQanava
- There is framework for everything.
- How can i make something like that in Qt5 ? (drag-drop and connect each other)
- Make a canvas with custom, interactable objects
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Should we add a sticky FAQ post?
This is not how to do it. You do not want to have business logic in qml. For this you can subclass QtQuickItem. https://github.com/cneben/QuickQanava is a good example for this.
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.
- free categorical predictive analytic software?
What are some alternatives?
nodeeditor - Qt Node Editor. Dataflow programming framework
MLP Classifier - A handwritten multilayer perceptron classifer using numpy.
libgrape-lite - 🍇 A C++ library for parallel graph processing (GRAPE) 🍇
scikit-learn - scikit-learn: machine learning in Python
vg - tools for working with genome variation graphs
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
fdg - A Force Directed Graph Drawing Library
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
tensorflow - An Open Source Machine Learning Framework for Everyone
netsci-labs - (In progress) Network science laboratories. Covers graph theory, random graphs and ML on graphs
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.