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www.exxactcorp.com | ||
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www.simula.no
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| | | | | Graph neural networks (GNNs), which extend the successful ideas of deep learning to irregularly structured data, are a recent addition to the field of artificial intelligence. While traditional deep learning has focused on regular inputs such as images composed of pixels in two-dimensional space, graph neural networks can analyze and learn from unstructured connections between objects. This gives GNNs the ability to tackle completely new classes of problems, such as analyzing social networks and power grids or uncovering molecule structures in computational chemistry. | |
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www.v7labs.com
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| | | | | Learn about the different types of neural network architectures. | |
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www.blopig.com
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| | | | | [AI summary] The article discusses the application of graph neural networks (GNNs) in protein property prediction, highlighting their ability to model protein structures and interactions, the integration of pre-trained protein language models like ESM, and the use of residual layers to address oversmoothing challenges. | |
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www.paepper.com
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| | | [AI summary] This article explains how to train a simple neural network using Numpy in Python without relying on frameworks like TensorFlow or PyTorch, focusing on the implementation of ReLU activation, weight initialization, and gradient descent for optimization. | ||
