The MLOps Blog

FEBRUARY 22, 2024

TL;DR: In many machine-learning projects, the model has to frequently be retrained to adapt to changing data or to personalize it. Continual learning is a set of approaches to train machine learning models incrementally, using data samples only once as they arrive. What is continual learning?

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Unite.AI

MAY 31, 2023

Hence, Liquid Neural Networks have two key features: Dynamic architecture: Its neurons are more expressive than the neurons of a regular neural network, making LNNs more interpretable. Hence, it becomes easier for researchers to explain how an LNN reached a decision.

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Viso.ai

MARCH 26, 2024

This enhances the interpretability of AI systems for applications in computer vision and natural language processing (NLP). The introduction of the Transformer model was a significant leap forward for the concept of attention in deep learning. The typical architecture of a neural machine translation model (NMT).

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Unite.AI

JULY 31, 2023

This approach is known as self-supervised learning , and it’s one of the most efficient methods to build ML and AI models that have the “ common sense ” or background knowledge to solve problems that are beyond the capabilities of AI models today.

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