Self Attention Transformers -- Complete Understanding with Code (Python)
Before Transformer architectures were introduced, Recurrent Neural Networks (RNNs) were used for sequential modelling tasks.
RNNs dealt with multiple challenges, some of which include:
Long term-dependencies are not preserved
Tokens that appear later in the sequence are not utilized in the context vector
Sequential Processing
In this video, we shall answer the 3 questions
Need for self-attention
Understanding self-attention and query, key and value vectors
Implementing self-attention in Python
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Additionally, here are few more useful resources for beginners and curious data enthusiasts
1. kaggle: a platform to learn, practice, compete and win cash prizes [https://www.kaggle.com/]
2. paperswithcode: website presenting the latest in machine learning and data science research and the code implementations [https://paperswithcode.com/]
3. google colab: a platform to run code, build machine learning solutions, explore the capabilities of GPU and TPU without any hassle in setting up the environment: [https://colab.research.google.com/]
Remember, "the greatest investment ever made is investment in self growth and learning"