Lecture 14: Simplified Attention Mechanism - Coded from scratch in Python | No trainable weights

In this lecture, we code a simplified attention mechanism from scratch, in Python. In the process, we learn about context vectors, attention scores and attention weights. We pay equal attention to theory, visual intuition and code.

0:00 Lecture objective
2:29 Context vectors
9:34 Coding embedding vectors in Python
14:45 What are attention scores?
19:18 Dot product and attention scores
22:57 Coding attention scores in Python
26:22 Simple normalisation
34:07 Softmax normalisation
37:34 Coding attention weights in Python
43:46 Context vector calculation visualised
50:19 Coding context vectors in Python
55:29 Coding attention score matrix for all queries
01:00:22 Coding attention weight matrix for all queries
01:04:27 Coding context vector matrix for all queries
01:14:10 Need for trainable weights in the attention mechanism


Link to code file: https://drive.google.com/file/d/1b5b2PG55PjSYWkPhHy4Q3mQwjBXmCOTX/view?usp=sharing

PyTorch Softmax Implementation: https://pytorch.org/docs/stable/generated/torch.nn.Softmax.html
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As we learn AI/ML/DL the material, we will share thoughts on what is actually useful in industry and what has become irrelevant. We will also share a lot of information on which subject contains open areas of research. Interested students can also start their research journey there.

Students who are confused or stuck in their ML journey, maybe courses and offline videos are not inspiring enough. What might inspire you is if you see someone else learning and implementing machine learning from scratch.

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