Masking in Gen AI Training: The Hidden Genius in Transformers

In this tutorial, we dive deep into the concept of masking and its critical role in training large language models. Learn why masking is essential, how it prevents cheating, and how triangular masks enable causal predictions. We'll also explore the mathematical foundations of masking, including its application in multi-head attention and dot product calculations.

Course Link HERE: https://sds.courses/genAI

You can also find us here:
Website: https://www.superdatascience.com/
Facebook:   / superdatascience  
Twitter:   / superdatasci  
LinkedIn:   / superdatascience  

Contact us at: [email protected]

Chapters
00:00 Introduction to Masking
00:30 Masking vs. Inference
01:04 Training Transformers with Masking
02:14 Full Sentence Training Approach
03:21 Multi-Head Attention & Context
04:18 Preventing Cheating with Masking
05:22 Architecture of Masking in Attention
06:33 Query-Key Indexing with Masking
07:37 Dot Products and Masking Math
08:47 Applying Negative Infinity in Masking
09:46 Weighted Sum and Softmax with Masks
11:18 Context-Aware Representations Explained
12:29 Triangular Masking Overview
13:04 Masking in Different Sentence Lengths
14:31 Creating Training Samples with Masking
15:35 Causal Masks in Transformers
16:08 Closing and Next Steps

#ai #MachineLearning #Transformers #LLM #Masking #DeepLearning #Tutorial #ArtificialIntelligence #NeuralNetworks #GPT #AITraining #LanguageModels #AIResearch #CausalMasking #TechTutorials

The video is an in-depth tutorial on the concept of masking in the training of large language models (LLMs). It explains how masking plays a critical role in preventing Transformers from "cheating" during training by looking at future words in a sentence.

The video covers:
The difference between the use of masking in inference and training processes.
How masking ensures that Transformers make accurate, context-aware predictions without relying on future information.
The concept of triangular masking, also known as causal masking, which hides future words to enable sequential, logical predictions in training.
The mathematical implementation of masking using dot products, negative infinity, and softmax functions to create masked attention.
How the multi-head attention mechanism works with masked sequences to generate context-aware vector representations for training.
The tutorial also highlights the importance of masking in training models like GPT, explaining why it's essential for creating accurate and robust AI systems.