Unite.AI

JULY 31, 2023

The SEER model by Facebook AI aims at maximizing the capabilities of self-supervised learning in the field of computer vision. The Need for Self-Supervised Learning in Computer Vision Data annotation or data labeling is a pre-processing stage in the development of machine learning & artificial intelligence models.

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Unite.AI

DECEMBER 26, 2024

These deep learning algorithms get data from the gyroscope and accelerometer inside a wearable device ideally worn around the neck or at the hip to monitor speed and angular changes across three dimensions.

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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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The MLOps Blog

APRIL 20, 2023

With over 3 years of experience in designing, building, and deploying computer vision (CV) models , I’ve realized people don’t focus enough on crucial aspects of building and deploying such complex systems. Hopefully, at the end of this blog, you will know a bit more about finding your way around computer vision projects.

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Mlearning.ai

JULY 11, 2023

You can use libraries like TensorFlow or PyTorch to practice building simple neural networks. Step 4: Learn About Different Deep Learning Architectures Deep learning offers various architectures for specific tasks. Step 6: Apply Deep Learning to Specific Domains Deep learning is used in many areas.

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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. Learn more by booking a demo. Vaswani et al.

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Viso.ai

APRIL 29, 2024

A Spatial Transformer Network (STN) is an effective method to achieve spatial invariance of a computer vision system. STNs are used to “teach” neural networks how to perform spatial transformations on input data to improve spatial invariance. What’s Next for Spatial Transformer Networks?

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