DataBloom AI is a leading contributor to Apache Wayang, developing "Blossom Sky," an AI-focused virtual data lakehouse platform that enables federated data access on the edge in order to train AI models directly at the source, uniquely combining the advantages of data lakes with data meshes.
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Based on our record, TensorFlow should be more popular than DataBloom AI. It has been mentiond 7 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Databloom.ai has released a dev environment: https://github.com/databloom-ai/BDE/blob/main/readme.md. Source: about 2 years ago
Blogpost via databloom.ai: Https://engineering.databloom.ai/2022/03/the-missing-piece-in-ml-based-query.html. Source: about 2 years ago
Artificial intelligence solutions have been revolutionizing the industry continuously in the last decades. The benefits delivered by these technologies are numerous and diverse; among others you can find: capacity to improve work efficiency, capacity to analyze big datasets, automate infrastructure for easy escalation, enhance customer experience, etc. Nowadays companies are challenging themselves to obtain... Source: about 2 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
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Keras - Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.
Statwing - Simply upload your spreadsheet or dataset, then select the relationships you want to explore. Statwing was built by and for analysts, so you can clean data, explore relationships, and create charts in minutes instead of hours.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.