Your AI system's ceiling is set by your data infrastructure quality. No model architecture improvement can break through that ceiling.

Hello! I'm Koshi, an active infrastructure engineer who unravels the 'whys' of AI technology. Last time, I explained the 'Feed-Forward Network (FFN)' that is placed after Self-Attention inside the ...

Earlier this year, I took the trip of a lifetime to Lapland, a winter wonderland above Finland’s Arctic Circle. Most of my time was spent with snow crunching under my winter boots, the Northern Lights ...

Machine Learning Practical - Coursework 2: Analysing problems with the VGG deep neural network architectures (with 8 and 38 hidden layers) on the CIFAR100 dataset by monitoring gradient flow during ...

Kaiming He, Yann LeCun, and others have announced a method to remove Transformer layer normalization layers while improving performance. The proposed method, 'Dynamic Tanh (DyT),' replaces layer ...

Transformers have revolutionized natural language processing as the foundation of large language models (LLMs), excelling in modeling long-range dependencies through self-attention mechanisms. However ...

Normalization techniques have become integral to the training of deep neural networks, serving to stabilise learning dynamics, accelerate convergence and improve generality. At their core, these ...

Abstract: In recent years, several normalization methods have been proposed in order to train neural networks, including batch normalization, layer normalization, weight normalization, and group ...

The Large Language Models (LLMs) are highly promising in Artificial Intelligence. However, despite training on large datasets covering various languages and topics, the ability to understand and ...