scCODA
TensorFlow-Examples
scCODA | TensorFlow-Examples | |
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1 | 2 | |
134 | 43,236 | |
3.7% | - | |
2.2 | 0.0 | |
about 1 year ago | 3 months ago | |
Jupyter Notebook | Jupyter Notebook | |
BSD 3-clause "New" or "Revised" License | GNU General Public License v3.0 or later |
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scCODA
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Are there any tools to measure changes in cell abundance between two conditions on single cell RNA seq data?
Hope it works for you! scCODA is another tool that does a similar compositions analysis
TensorFlow-Examples
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Keras vs. TensorFlow
A linear regression model
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Tensorman and RTX 30-Series GPU's
When I run this simple project, the log output is below. There is a 5-minute pause at 16:48. There is a second pause at the end of the script before the output of the example (final output excluded). This project runs quickly if I exclude "--gpu" and run it on the CPU.
What are some alternatives?
popscle - A suite of population scale analysis tools for single-cell genomics data including implementation of Demuxlet / Freemuxlet methods and auxilary tools
lego-mindstorms - My LEGO MINDSTORMS projects (using set 51515 electronics)
miloR - R package implementation of Milo for testing for differential abundance in KNN graphs
graphkit-learn - A python package for graph kernels, graph edit distances, and graph pre-image problem.
danbooru-tags - An analysis of tags and metadata on Danbooru
pyVHR - Python framework for Virtual Heart Rate
TensorFlow-Tutorials - TensorFlow Tutorials with YouTube Videos
Deep-Learning-Hardware-Benchmark - This repository contains the proposed implementation for benchmarking in order to evaluate whether a setup of hardware is feasible for deep learning projects.
rmi - A learned index structure
Deep-Learning-With-TensorFlow-Blog-series - All the resources and hands-on exercises for you to get started with Deep Learning in TensorFlow [Moved to: https://github.com/Rishit-dagli/Deep-Learning-With-TensorFlow]
TF_JAX_tutorials - All about the fundamental blocks of TF and JAX!
models - A collection of pre-trained, state-of-the-art models in the ONNX format