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neptune.ai | ||
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blog.fastforwardlabs.com
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| | | | | We discussed this research as part of our virtual event on Wednesday, July 24th; you can watch the replay here! Convolutional Neural Networks (CNNs or ConvNets) excel at learning meaningful representations of features and concepts within images. These capabilities make CNNs extremely valuable for solving problems in the image analysis domain. We can automatically identify defects in manufactured items, reducing costs associated with quality assurance processes; we can now infer depth information and reconstruct 3D maps from 2D images without additional metadata, giving new potential to urban planning as well as entertainment experiences; we can perform pixel level separation of objects in images and video with applications ranging from public safety to medical robotics; and we can perform "super resolution" on photo images (upscale an image up to 10x) with a trained deep-learning system reconstructing, filling in, and thus sharpening images with information that would be omitted and lost using a standard digital zoom. | |
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deepmind.google
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| | | | | This has been a year of incredible progress in the field of Artificial Intelligence (AI) research and its practical applications. | |
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haifengl.wordpress.com
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| | | | | Generative artificial intelligence (GenAI), especially ChatGPT, captures everyone's attention. The transformerbased large language models (LLMs), trained on a vast quantity of unlabeled data at scale, demonstrate the ability to generalize to many different tasks. To understand why LLMs are so powerful, we will deep dive into how they work in this post. LLM Evolutionary Tree... | |
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pyimagesearch.com
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| | | In this tutorial, you will receive a gentle introduction to training your first Convolutional Neural Network (CNN) using the PyTorch deep learning library. | ||
