Masking in Gen AI Training: The Hidden Genius in Transformers скачать в хорошем качестве

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: support@superdatascience.com 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.