Here’s the Neural Network That Can Predict Your Next Online Friend

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This article focuses on training a machine learning model for link prediction using Graph Neural Networks (GNNs) on the Twitch dataset.
They choose to use Relational Graph Convolutional Network (R-GCN) model for datasets with multiple node and edge types to handle node properties that may vary.
Hyperparameters like learning rate, number of hidden units, number of epochs, batch size, and negative sampling are crucial and can impact the model's performance.
The model training process involves creating specific roles for Neptune and SageMaker, setting up IAM roles, and using Neptune ML API for starting model training.
Model training involves tuning parameters like learning rate, hidden units, epochs, batch size, negative sampling, dropout, and regularization coefficient.
The status of the model training job can be checked using the Neptune cluster's HTTP API, and results are reviewed in the AWS console, specifically in SageMaker Training Jobs.
The article demonstrates comparing hyperparameters used in different training jobs, showcasing how variations in parameters affect model accuracy.
Model artifacts, training stats, and metrics are stored in the output S3 bucket, essential for creating an inference endpoint and making actual link predictions.
The completion of model training sets the stage for generating link predictions based on the trained model's artifacts.

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