Technical Breakdown
Misty card reading leverages a novel neural network architecture that emulates the analytical processes of human tarot card readers. The algorithm takes a sequence of cards, each represented as a tensor, as input. Convolutional layers extract features from the cards, while recurrent layers learn the temporal relationships between them. The output is a probability distribution representing the interpretation of the reading in the context of the user’s inquiry.
Performance Insights
Misty card reading is highly scalable, handling readings of variable card counts efficiently. The model’s accuracy increases linearly with the number of training epochs, reaching a peak after 100 epochs. Cross-validation results indicate a robust performance across different datasets, with an average accuracy of 85% for 3-card readings and 78% for 7-card readings. The runtime remains consistent regardless of the card count, providing a real-time experience for users.
