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Coding the entire LLM Transformer Block
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In this lecture, we code the entire Transformer block in Python based on it’s 5 components:
(1) Multi head attention
(2) Layer normalization
(3) Dropout layer
(4) Feedforward neural network with GELU activation
(5) Shortcut connections
We understand the theory, mathematical intuition and also do the coding for the entire implementation.
0:00 Transformer block visualised
3:56 5 components of the transformer block
16:28 Transformer block shape preservation
19:34 Let us jump into code!
21:14 Coding LayerNorm and FeedForward Neural Network class
25:40 Coding the transformer block class in Python
33:57 Transformer block code summary
35:12 Testing the transformer class using simple example
41:09 Lecture summary and next steps
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Vizuara philosophy:
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.
No cost. No hidden charges. Pure old school teaching and learning.
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🌟 Meet Our Team: 🌟
🎓 Dr. Raj Dandekar (MIT PhD, IIT Madras department topper)
🎓 Dr. Rajat Dandekar (Purdue PhD, IIT Madras department gold medalist)
🎓 Dr. Sreedath Panat (MIT PhD, IIT Madras department gold medalist)
🎓 Sahil Pocker (Machine Learning Engineer at Vizuara)
🎓 Abhijeet Singh (Software Developer at Vizuara, GSOC 24, SOB 23)
🎓 Sourav Jana (Software Developer at Vizuara)
(1) Multi head attention
(2) Layer normalization
(3) Dropout layer
(4) Feedforward neural network with GELU activation
(5) Shortcut connections
We understand the theory, mathematical intuition and also do the coding for the entire implementation.
0:00 Transformer block visualised
3:56 5 components of the transformer block
16:28 Transformer block shape preservation
19:34 Let us jump into code!
21:14 Coding LayerNorm and FeedForward Neural Network class
25:40 Coding the transformer block class in Python
33:57 Transformer block code summary
35:12 Testing the transformer class using simple example
41:09 Lecture summary and next steps
=================================================
=================================================
Vizuara philosophy:
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.
No cost. No hidden charges. Pure old school teaching and learning.
=================================================
🌟 Meet Our Team: 🌟
🎓 Dr. Raj Dandekar (MIT PhD, IIT Madras department topper)
🎓 Dr. Rajat Dandekar (Purdue PhD, IIT Madras department gold medalist)
🎓 Dr. Sreedath Panat (MIT PhD, IIT Madras department gold medalist)
🎓 Sahil Pocker (Machine Learning Engineer at Vizuara)
🎓 Abhijeet Singh (Software Developer at Vizuara, GSOC 24, SOB 23)
🎓 Sourav Jana (Software Developer at Vizuara)
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