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SEER: A Breakthrough in Self-Supervised Computer Vision Models?

Unite.AI

Self-supervised learning has already shown its results in Natural Language Processing as it has allowed developers to train large models that can work with an enormous amount of data, and has led to several breakthroughs in fields of natural language inference, machine translation, and question answering.

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Vision Transformers (ViTs) vs Convolutional Neural Networks (CNNs) in AI Image Processing

Marktechpost

Vision Transformers (ViT) and Convolutional Neural Networks (CNN) have emerged as key players in image processing in the competitive landscape of machine learning technologies. The Rise of Vision Transformers (ViTs) Vision Transformers represent a revolutionary shift in how machines process images.

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data2vec: A Milestone in Self-Supervised Learning

Unite.AI

To overcome the challenge presented by single modality models & algorithms, Meta AI released the data2vec, an algorithm that uses the same learning methodology for either computer vision , NLP or speech. For example, there are vocabulary of speech units in speech processing that can define a self-supervised learning task in NLP.

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Top AI Courses Offered by Intel

Marktechpost

Its AI courses offer hands-on training for real-world applications, enabling learners to effectively use Intel’s portfolio in deep learning, computer vision, and more. By the end, students will understand network construction, kernels, and expanding networks using transfer learning.

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A Guide to Convolutional Neural Networks

Heartbeat

In this guide, we’ll talk about Convolutional Neural Networks, how to train a CNN, what applications CNNs can be used for, and best practices for using CNNs. What Are Convolutional Neural Networks CNN? CNNs learn geometric properties on different scales by applying convolutional filters to input data.

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DenseFormer by EPFL Researchers: Enhancing Transformer Efficiency with Depth-Weighted Averages for Superior Language Modeling Performance and Speed

Marktechpost

The transformer architecture has improved natural language processing, with recent advancements achieved through scaling efforts from millions to billion-parameter models. However, larger models’ increased computational cost and memory footprint limit their practicality, benefiting only a few major corporations.

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Mini-Gemini: A Simple and Effective Artificial Intelligence Framework Enhancing multi-modality Vision Language Models (VLMs)

Marktechpost

Vision Language Models (VLMs) emerge as a result of a unique integration of Computer Vision (CV) and Natural Language Processing (NLP). It utilizes patch info mining for detailed visual cue extraction.