Transformers EXPLAINED! Neural Networks | | Encoder | Decoder | Attention

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Transformers Explained! This architecture came from this amazing paper: Attention is all you need. Link here: https://arxiv.org/abs/1706.03762

Parts of this architecture is used for state-of-the-art technologies such as GPT-3 and variations of BERT. So, you must know what a transformer model is if you want to dive further into the more advanced methods since they all build upon the principles of the transformer model!

I explain what you NEED to know and nothing more!

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Watch Next?
Named Entity Recognition → https://youtu.be/4pCB1lZrBcQ
Text Cleaning and Preprocessing → https://youtu.be/ZucclQNVBlo

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📓 Requirements 🧐
Python
Jupyter notebok

⌛ Timeline ⌛
0:00 - What is a Transformer? Additional Resources
1:41 - Why use a Transformer architecture?
3:17 - Encoder Block
7:52 - Decoder Block
10:20 - Multi-head Attention Mechanisms Explained Further

🏷️Tags🏷️:
Python,Transformers,Transformer,Machine Learning, Deep Learning, Encoder, Decoder, Attention, multi-head attention, embedding, Natural Language Processing, natural, language, processing, word, token, Normalization,Positional encoding,layers,softmax,output,probabilities,feed-forward network,network,tutorial,how to,instruction,gpt-3,building block,BERT

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