Deep Learning Model Architecture and Training

implementationChallenge

Prompt Content

Implement a deep learning model (e.g., LSTM-Autoencoder or Transformer-based) for unsupervised anomaly detection on time-series telemetry. Train this model using the prepared synthetic dataset. Document your choice of architecture, loss function, and optimization strategy. Use Weights & Biases to track your training process, hyperparameter tuning, and visualize metrics like reconstruction error or anomaly scores.

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