Based on our record, Amazon Comprehend should be more popular than TensorFlow. It has been mentiond 19 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.
Would you like additional capabilities like connecting to Machine Learning, Dashboards and Quicksight and leveraging other tools like Comprehend. - Source: dev.to / 10 months ago
Once again, I asked ChatGPT to perform this analysis. I could have used some of the AI tools provided by AWS, like the detectSentiment API from Amazon Comprehend, but tools like ChatGPT make it so easy to just add a simple "also, tell me in one word what the sentiment is" clause to a query I'm asking. - Source: dev.to / 12 months ago
And now we can run amplify push to create the resources in AWS. The AWS service that will be used for this functionality is Amazon Comprehend. The pricing for this service can be found here. - Source: dev.to / about 1 year ago
Amazon has developed its own NLP service called Amazon Comprehend, which is designed to extract insights and relationships from unstructured text data. Source: about 1 year ago
First, can you use a different AWS service, such as Comprehend or SageMaker? You only "pay for what you use" instead of paying for an idle server. This is especially helpful for a start up, since you don't pay a lot if you don't have a lot of customers.. Source: about 1 year 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 / about 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
spaCy - spaCy is a library for advanced natural language processing in Python and Cython.
PyTorch - Open source deep learning platform that provides a seamless path from research prototyping to...
FuzzyWuzzy - FuzzyWuzzy is a Fuzzy String Matching in Python that uses Levenshtein Distance to calculate the differences between sequences.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
Google Cloud Natural Language API - Natural language API using Google machine learning
Keras - Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.