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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Heartbeat

NOVEMBER 27, 2023

Deep learning automates and improves medical picture analysis. Convolutional neural networks (CNNs) can learn complicated patterns and features from enormous datasets, emulating the human visual system. Convolutional Neural Networks (CNNs) Deep learning in medical image analysis relies on CNNs.

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Viso.ai

APRIL 29, 2024

STNs are used to “teach” neural networks how to perform spatial transformations on input data to improve spatial invariance. Spatial Transformer Networks Explained The central component of the STN is the spatial transformer module. What’s Next for Spatial Transformer Networks?

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Heartbeat

SEPTEMBER 25, 2023

One remarkable advancement of machine learning is the ability to analyze and interpret medical images with unparalleled accuracy and speed. Machine learning algorithms can continuously learn and update treatment models by incorporating data on how patients respond to different therapies.

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Viso.ai

MARCH 26, 2024

Furthermore, attention mechanisms work to enhance the explainability or interpretability of AI models. Attention Mechanisms in Deep Learning Attention mechanisms are helping reimagine both convolutional neural networks ( CNNs ) and sequence models.

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Unite.AI

JULY 31, 2023

The ultimate goal of the SEER model is to help in developing strategies for the pre-training process that use uncurated data to deliver top-notch state of the art performance in transfer learning. An algorithm that can learn the patterns from a large amount of images without any labels, annotations, or metadata.

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