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Continual Learning: Methods and Application

The MLOps Blog

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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A Step-by-Step Guide to Learning Deep Learning

Mlearning.ai

Deep learning has transformed artificial intelligence, allowing machines to learn and make smart decisions. If you’re interested in exploring deep learning, this step-by-step guide will help you learn the basics and develop the necessary skills. Also, learn about common algorithms used in machine learning.

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Unpacking the Power of Attention Mechanisms in Deep Learning

Viso.ai

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. Vaswani et al. It does this by applying self-attention to sequences of image patches.

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The 11 Top AI Influencers to Watch in 2024 (Guide)

Viso.ai

From the development of sophisticated object detection algorithms to the rise of convolutional neural networks (CNNs) for image classification to innovations in facial recognition technology, applications of computer vision are transforming entire industries. Thus, positioning him as one of the top AI influencers in the world.

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Midjourney vs. Stable Diffusion: Which Should You Use?

Viso.ai

Viso Suite, the all-in-one computer vision solution The journey of AI in art traces back to the development of neural networks and deep learning technologies. And, Generative Adversarial Networks (GANs) , which opened new doors for generating high-quality, realistic images.