colly VS Gorgonia

SerpApi focuses on scraping search results. That's why we need extra help to scrape individual sites. We'll use GoColly package.

Interesting. Can you compare it to colly? [0]

Last time I looked it was the most popular choice for scraping in Go and I have some projects using it.

Is it similar? Does it have more/less features or is it more suited for a different use case? (Which one?)

[0] https://github.com/gocolly/colly

Sounds cool, but how is this different from Colly: https://github.com/gocolly/colly?

We could write some snippets mixing all these, but the best option in real life is to use a tool with it all, like Scrapy, pyspider, node-crawler (Node.js), or Colly (Go).

Unless you're specifically trying to do it using net/http, I recommend using colly. I've used it in a few scrappers and I love it!

In this blog, we will be covering the basics of web scraping in Go using the Fiber and Colly frameworks. Colly is an open-source web scraping framework written in Go. It provides a simple and flexible API for performing web scraping tasks, making it a popular choice among Go developers. Colly uses Go's concurrency features to efficiently handle multiple requests and extract data from websites. It offers a wide range of customization options, including the ability to set request headers, handle cookies, follow redirects, and more

github.com/gocolly/colly - popular and widely-used library for web scraping in Go. It provides a higher-level API than net/http and makes it easier to extract information from websites. It also provides features such as concurrency, automatic request retries, and support for cookies and sessions.

GitHub - gorgonia/gorgonia: Gorgonia is a library that helps facilitate machine learning in Go.

I did end up writing and using a custom library for Random Forest (it's also in AwesomGo) in one real-world project (detecting Alzheimer's and Parkinson's from speech from a mobile app) - https://github.com/malaschitz/randomForest I had better results than the team who used TensorFlow and most importantly I didn't have to use any other technology than Go. For NN's it's probably best to use https://gorgonia.org/ - but it's not exactly a user friendly library. But there is a whole book on it - Hands-On Deep Learning with Go.

Perhaps it's a product of French culture, but because Gorgonia[0] has a number of French contributors, this was actually the way we structured our documentation.

But this is the first time I've heard of the name of the framework.

[0]: https://gorgonia.org

Oh it's.Gorgonia

Website: https://gorgonia.org

I use Gorgonia.

What you think about this https://github.com/gorgonia/gorgonia ? I also recall there is something else out there but can't find it at the moment...

Yep, all of them: https://github.com/gorgonia/gorgonia