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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Marktechpost

DECEMBER 10, 2024

Biological Neural Networks While ANNs are inspired by biological neural networks, there are notable differences: Feature Biological Neural Network Artificial Neural Network Neurons Billions of biological neurons. Learning Context-aware, continuous learning.

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Pickl AI

SEPTEMBER 3, 2024

For example, convolutional neural networks (CNNs), a specific type of ANN, are widely used for image classification tasks, enabling applications such as facial recognition and object detection. Continuous Learning Given the rapid pace of advancements in the field, a commitment to continuous learning is essential.

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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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Pickl AI

NOVEMBER 28, 2024

Understanding various Machine Learning algorithms is crucial for effective problem-solving. Continuous learning is essential to keep pace with advancements in Machine Learning technologies. The system learns to take actions that maximise cumulative rewards, making it ideal for sequential decision-making tasks.

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Viso.ai

MARCH 26, 2024

This enhances their ability to process sequences in tasks like robotic navigation and augmented reality applications. Attention Mechanisms in Deep Learning Attention mechanisms are helping reimagine both convolutional neural networks ( CNNs ) and sequence models.

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The MLOps Blog

APRIL 20, 2023

While convolutional neural networks (CNNs) are commonly used in smaller-scale facial recognition systems, scaling to a larger number of faces requires a more sophisticated approach. Edge deployment can be particularly useful in scenarios where real-time processing is essential, such as autonomous vehicles or robotics.

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