Day by day the number of machine learning models is increasing at a pace. With this increasing rate, it is hard for beginners to choose an effective model to perform Natural Language Understanding (NLU) and Natural Language Generation (NLG) mechanisms. By Purnasai Gudikandula.
The wide availability of other sequence-to-sequence learning models like RNNs, LSTMs, and GRU always raises a challenge for beginners when they think about transformers. And it is important to a beginner to have basic knowledge of these models before they get to know more about transformers. If you still struggle why not RNNs, LSTMs, and GRUs, here we listed a few important points that can help you out.
The article does a good job of explaining:
- Why Transformers?
- Transformer architecture
- Transformer architecture flow
- Code for language translation
- Transformers for tomorrow
Source: ARXIV via https://deeplobe.ai/machine-learning-for-transformers-explained-with-language-translation/
Transformers are pretty versatile and can be used in a variety of NLP tasks, such as; Machine Translation, Text Summarization, Speech Recognition, Question-Answering systems, and so on. Well worth your time!
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