Скачать или смотреть Coding the entire LLM Transformer Block

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.

The key reference book which this video series very closely follows is Build a Large Language Model from Scratch by Manning Publications. All schematics and their descriptions are borrowed from this incredible book!

This book serves as a comprehensive guide to understanding and building large language models, covering key concepts, techniques, and implementations.

Affiliate links for purchasing the book will be added soon. Stay tuned for updates!

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

Link to code file: https://drive.google.com/file/d/1k4Tw...

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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.

No cost. No hidden charges. Pure old school teaching and learning.

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