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Also, many tools only talk SQL. Shillelagh was developed for Apache Superset, a powerful open source business intelligence web application, and allows it to query an infinitude of new data sources without having to change a single line of code in Superset. Source: almost 2 years ago
I'm adding this to Apache Superset today! Source: almost 2 years ago
I also like to do some data analysis on the side and recently ran across Apache Superset which describes itself as a "modern data exploration and data visualization platform". Coincidentally, Superset has a lot of Python code and can be deployed in containers (nine of them at current count!). - Source: dev.to / about 2 years ago
Please don't hesitate to like and bookmark this post, write a comment, and give a star to Cube and Superset on GitHub. I hope these tools would be a part of your toolkit when you decide to build a metrics store and a business intelligence application on top of it. - Source: dev.to / over 2 years ago
Discussion by kgabryje at apache / superset “feat(native-filters): add search all filter options #14710“. - Source: dev.to / almost 3 years ago
Converting the images to a tensor: Deep learning models work with tensors, so the images should be converted to tensors. This can be done using the to_tensor function from the PyTorch library or convert_to_tensor from the Tensorflow library. - Source: dev.to / over 1 year ago
So I went to tensorflow.org to find some function that can generate a CSR representation of a matrix, and I found this function https://www.tensorflow.org/api_docs/python/tf/raw_ops/DenseToCSRSparseMatrix. Source: almost 2 years ago
Can anyone offer up an explanation for why there is a performance difference, and if possible, what could be done to fix it. I'm using the installation guidelines found on tensorflow.org and installing tf2.7 through pip using an anaconda3 env. Source: about 2 years ago
I don't have much experience with TensorFlow, but I'd recommend starting with TensorFlow.org. Source: about 2 years ago
I have looked at this TensorFlow website and TensorFlow.org and some of the examples are written by others, and it seems that I am stuck in RNNs. What is the best way to install TensorFlow, to follow the documentation and learn the methods in RNNs in Python? Is there a good tutorial/resource? Source: about 2 years ago
Mage AI - Open-source data pipeline tool for transforming and integrating data.
PyTorch - Open source deep learning platform that provides a seamless path from research prototyping to...
Aha! Visual Chart Tool - Create beautiful product roadmap visualizations
Keras - Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.
Apache Superset - modern, enterprise-ready business intelligence web application
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.