Diyotta
Striim
HVR
Celigo Data Loader
Oracle Data Integrator
Bryteflow Data Replication and Integration
alooma
Astera Centerprise
TensorFlow
PyTorch
Scikit-learn
Keras
IBM Watson Studio
OpenCV
Amazon SageMaker
Pega Platform
Diyotta is an enterprise-class data integration platform that connects enterprises to all their data. It provides a single integrated platform that makes it easy to quickly and efficiently integrate enormous volumes of data from any source to any target, whether on-premises, in the cloud, or a hybrid environment. Diyotta is built for modern data architectures where data can be processed in batch, or real-time streams, in the most optimized fashion. It also scales in all dimensions with its agent-based architecture and optimizes the data movement across several data-points easily and efficiently.
Diyotta accelerates time to value for new investments in big data platforms and ongoing modernization of data warehouses. With Diyotta, companies fully leverage their existing platform investment, move to modern data platforms with the highest level of reuse possible, and quickly respond to business needs for new data and analytics.
Diyotta TensorFlowBased on our record, TensorFlow seems to be more popular. 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.
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: almost 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
Striim - Striim provides an end-to-end, real-time data integration and streaming analytics platform.
PyTorch - Open source deep learning platform that provides a seamless path from research prototyping to...
HVR - Your data. Where you need it. HVR is the leading independent real-time data replication solution that offers efficient data integration for cloud and more.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
Celigo Data Loader - Celigo is an advanced platform that comes with exclusive service data loading in a smooth and effective way.
Keras - Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.
