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ai.googleblog.com | ||
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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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www.altexsoft.com
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| | | | | A dive into the machine learning pipeline on the production stage: the description of architecture, tools, and general flow of the model deployment. | |
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blog.fastforwardlabs.com
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| | | | | Graph Neural Networks (GNNs) are neural networks that take graphs as inputs. These models operate on the relational information in data to produce insights not possible in other neural network architectures and algorithms. While there is much excitement in the deep learning community around GNNs, in industry circles, this is sometimes less so. So, I'll review a few exciting applications empowered by GNNs. Overview of Graphs and GNNs A graph (sometimes called a network) is a data structure that highlights the relationships between components in the data. | |
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journal.everypixel.com
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| | | Discover AI image statistics: the total number of AI images, the number of images created with Stable Diffusion, Adobe Firefly, Midjourney, DALL-E 2, and more. | ||
