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BrowserCat's answer:
BrowserCat is built on robust open source technology that's under active development. The star of the show is Playwright, which is our recommended automation library. It's maintained by Microsoft, it officially supports JS, Python, Java, and .NET, and it's fast becoming the industry standard. BrowserCat also supports Puppeteer and numerous unofficial Playwright ports to Go, Rust, PHP, and Ruby.
BrowserCat's answer:
Unlike other headless browser providers, BrowserCat gives you total control over your browser instances for as long as you need them. Leverage the browsers cache, cookies, and storage for bespoke browser automation jobs that truly differentiate your business from the competition.
BrowserCat's answer:
In previous corporate and startup gigs, I faced the challenge of developing robust, fast, and scalable browser automation. Most APIs in the space are too limiting for our needs and they were often incredibly slow. On the other hand, hosting your own headless browser fleet was a pain. I founded BrowserCat to make scaling up browser automation as easy, reliable, and affordable as deploying a serverless function.
BrowserCat's answer:
We primarily serve developers, whether the seek to develop unique browser automation jobs or radically improve the performance of their integration tests. However, we frequently work with management, biz ops, and product leaders to solve problems they can't solve any way but through automation.
BrowserCat's answer:
BrowserCat is built for performance, scalability, stability, and affordability using modern web technologies. Many of our competitors were early to market and compete on entrenchment rather than functionality. Still others are bound by their existing users to continue supporting legacy tech, rather than embrace improved, modern standards. BrowserCat is focused on supporting your for the next ten years, rather than the past ten years.
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 2 years 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 3 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 3 years ago
I don't have much experience with TensorFlow, but I'd recommend starting with TensorFlow.org. Source: about 3 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 3 years ago
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