A highly-specialized online tool, Price2Spy is launched back in 2011 and is now used by more than 680 companies of all sizes, worldwide.
It helps eCommerce professionals to monitor, track and analyze their competitors' or retailers' product pricing and availability. Users are offered both pricing acquisition as well as multiple reporting mechanisms for analyzing data.
Price2Spy is based on 4 main mechanisms (price comparison, price change alerts, pricing analytics, and repricing), it provides essential aid – both in everyday pricing operations (an email alert each time it detects a price or availability change) and in strategic decision-making.
With advanced features like B2B price checks (prices protected by username/password), in-cart price capturing, and stealth IP monitoring, it represents a state-of-the-art solution when it comes to price monitoring.
Price2Spy is even capable of monitoring websites that are built to shield off monitoring applications. You can virtually see the pricing of your competition even if their websites don’t want to be monitored.
The Repricing module enables you to define your own pricing strategies identity which products can go up / down in price, and get these prices changed in your online store.
There is little to be done from your end to get the system up and running. Price2Spy offers tutorials, demos, and online support to help users along the way.
Based 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: 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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