User:A very nice name/Evaluate an Article

Which article are you evaluating?
TensorFlow

Why you have chosen this article to evaluate?
(Briefly explain why you chose it, why it matters, and what your preliminary impression of it was.)

I chose to evaluate the TensorFlow because I have learned and used it in a course provided by Hunter. It matters because AI has been evolving very quickly in the past few years, and TensorFlow and the neural network it focuses on is a very important aspect of AI. I expect the article to be long and thorough, as TensorFlow is a widely used tool in the fields of AI; I want to pay close attention to how a well-defined topic looks in Wikipedia. I am also eager to learn something new from this article!

Evaluate the article
(Compose a detailed evaluation of the article here, considering each of the key aspects listed above. Consider the guiding questions, and check out the examples of what a useful Wikipedia article evaluation looks like.)

The first paragraph of the lead section allows readers to quickly grasp what TensorFlow as a technology is. The rest of the lead section talks about the brief history of TensorFlow and some technologies that can be used with it. It does go over a preview of the major components quickly and concisely.

The contents of this article are up to date and relevant to the topic. I do not know if there is any content that is missing without doing a research, but I am pretty happy with the amount and variety of the content I saw on the page. The article is also about a technology, therefore it does not deal with one of Wikipedia's equity gap.

The article is written in a neutral tone. It describes the technology in the same way one will objectively define a tool like calculator.

There are many sources in this article, many of the sentences are cited using multiple sources. Additionally, one of the main source this article use is directly from TensorFlow, making it the best source possible to describe TensorFlow. All the links I clicked on worked.

The article is well organized, well-written, and no grammatical errors as far as I can tell.

There are no images in the main body of the article., which makes sense to me because it's talking about things like metrics and histories that are hard to visualize with pictures.

In the talk page, one editor brought up about how drawbacks and negative information of TensorFlow is not mentioned in the article. I do see a constructive conversation going back and forth among a few editors, and the majority opinion turns out to be that the article is perfectly fine as is and the so-called negative information has no place to be on the page.

The overall status of the article is great for those who wish to get a brief understanding of different aspects of TensorFlow. It is precise and on point, but at the same time this means that the article does not go too in depth about use cases of TensorFlow. This seems fine to me, if one intends to learn about the usage they can easily go to the TensorFlow's official documentation page, which is a common practice among computer scientists.