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Understanding LSTM Networks -- colah's blog
Source: colah.github.io
Article summary:
1. Recurrent Neural Networks (RNNs) are networks with loops that allow information to persist, and can be used for tasks such as classifying events in a movie.
2. Long Short Term Memory (LSTM) networks are a special type of RNN that can learn long-term dependencies, and have been used successfully in many applications.
3. LSTMs are explicitly designed to avoid the long-term dependency problem, and have become widely used due to their effectiveness.
Article analysis:
The article is written by Christopher Olah, a research scientist at Google Brain, which gives it some credibility. The article is well-written and provides an overview of Recurrent Neural Networks (RNNs) and Long Short Term Memory (LSTM) networks. It also provides examples of how these networks can be used in various applications such as speech recognition, language modeling, translation, image captioning etc., which makes it more reliable.
However, the article does not provide any evidence or sources for its claims about the effectiveness of LSTMs or any other application mentioned in the article. It also does not explore any counterarguments or potential risks associated with using these networks. Additionally, there is no mention of any potential biases or one-sided reporting in the article which could make it less reliable.
In conclusion, while the article provides an overview of RNNs and LSTMs and their applications, it lacks evidence for its claims and does not explore any potential risks associated with using these networks which makes it less reliable than other sources on this topic.