scanpy
Keras
| scanpy | Keras | |
|---|---|---|
| 5 | 78 | |
| 1,763 | 60,972 | |
| 2.0% | 0.3% | |
| 9.3 | 9.9 | |
| 2 days ago | 2 days ago | |
| Python | Python | |
| BSD 3-clause "New" or "Revised" 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.
scanpy
- Renaming Genes for Scanpy Plot
- Useful Python Decorators for Data Scientists
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[Scanpy] Installation issues related to pytables
Upon searching stack, I still do not understand the exact issue, as the file "hdf5extension.cp38-win_amd64" is present within C:\Users\username\anaconda3\lib\site-packages\tables\. Would anyone be able to explain the problem and any potential circumventions?
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standardize/normalize seq data
I would suggest you explore with SCANPY and verify if your batch labels generate a strong separation in your samples (PCA, tSNE, UMAP). If you then need to correct for batches, according to how simple/complex they are, you can choose a tool from this benchmark.
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Flipping one histogram below the axis.
I am plotting 2 1-D histograms on top of one another using a hold on command. Is there a way to have one histogram be upside down, and then to flip the entire plot 90˚? I am looking to create a violin plot https://github.com/theislab/scanpy/issues/1448 on my own, having one histogram on the left, and one on the right.
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?
scikit-learn - scikit-learn: machine learning in Python
MLP Classifier - A handwritten multilayer perceptron classifer using numpy.
dash - Data Apps & Dashboards for Python. No JavaScript Required.
data-science-ipython-notebooks - Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
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
deepvariant - DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.
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
dash-cytoscape - Interactive network visualization in Python and Dash, powered by Cytoscape.js
tensorflow - An Open Source Machine Learning Framework for Everyone
pagoda2 - R package for analyzing and interactively exploring large-scale single-cell RNA-seq datasets
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.