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How to use GPT-4o to train smaller AI models

How to use GPT-4o to train smaller AI models
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ETRI Unveils Ultra-Fast Generative Visual Intelligence Model: Creates Images in Just 2 Seconds

The Future of ML in Edge Equipment - Circuit Cellar

Internet of Things (IoT) has fueled increased demand for devices with greater intelligence, specifically machine learning (ML), at the network edge.

Knowledge Distillation and Continual Learning for Optimized Deep Neura by Vu Minh Hieu Phan

Over the past few years, deep learning (DL) has been achieving state-of-theart performance on various human tasks such as speech generation, language translation, image segmentation, and object detection. While traditional machine learning models require hand-crafted features, deep learning algorithms can automatically extract discriminative features and learn complex knowledge from large datasets. This powerful learning ability makes deep learning models attractive to both academia and big corporations. Despite their popularity, deep learning methods still have two main limitations: large memory consumption and catastrophic knowledge forgetting. First, DL algorithms use very deep neural networks (DNNs) with many billion parameters, which have a big model size and a slow inference speed. This restricts the application of DNNs in resource-constraint devices such as mobile phones and autonomous vehicles. Second, DNNs are known to suffer from catastrophic forgetting. When incrementally le

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