Keras
matplotlib
Keras | matplotlib | |
---|---|---|
78 | 36 | |
60,972 | 19,310 | |
0.3% | 1.1% | |
9.9 | 10.0 | |
3 days ago | 3 days ago | |
Python | Python | |
Apache License 2.0 | Python 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.
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.
matplotlib
- How and where is matplotlib package making use of PySide?
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Top 10 growing data visualization libraries in Python in 2023
Github: https://github.com/matplotlib/matplotlib
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Tkinter, PyGame windows too large on Mac
as suggested here.
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[OC] Attempted & Completed Suicide Rate in Canada, 1998/99
Tool: Matplotlib Pyplot
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Help unpickling an old dataset
The issue was described here: https://github.com/matplotlib/matplotlib/issues/8409, but the "solution" was just "this is fixed" which was not helpful to me.
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The Python Packages That Gave Me Nightmares: A Guide to Overcoming Common Challenges
Matplotlib: Matplotlib is a 2D plotting library that allows you to create visualizations of your data. It's a powerful tool for data analysis, but the syntax can be complex and the customization options can be overwhelming. GitHub - https://github.com/matplotlib/matplotlib
- pcolormesh very slow when using "log" axes
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Question: What is matplotlib short for?
A quick google shows: this history.txt:
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Linear Regression
Let's take a small subset i.e 20 data points of our prediction and compare it with actual output using matplotlib library
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Where to find a dynamic charge density animation/simulation?
I will think more about what I want to say next, but for now, I would like to say that I need the super-particles and PIC methods as I think that is the way forward for me. Are there ways to implement these methods in matplotlib, Visit or Paraview? Do I take existing code and import it into those programs to visualize it? Or can I directly program/simulate something in those visualizion tools without needing to import any code?
What are some alternatives?
MLP Classifier - A handwritten multilayer perceptron classifer using numpy.
PyQtGraph - Fast data visualization and GUI tools for scientific / engineering applications
scikit-learn - scikit-learn: machine learning in Python
plotly - The interactive graphing library for Python :sparkles: This project now includes Plotly Express!
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
pygal - PYthon svg GrAph plotting 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
bqplot - Plotting library for IPython/Jupyter notebooks
tensorflow - An Open Source Machine Learning Framework for Everyone
bokeh - Interactive Data Visualization in the browser, from Python
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
plotnine - A Grammar of Graphics for Python