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Structural principles and design patterns for artificial neural networks that define layers, connectivity, and activation functions.
Neural network architecture defines the structure of artificial neural networks, including layers, connectivity patterns, and activation functions. It governs learning capacity, generalization, and computational efficiency in machine learning systems. It is central to applications like computer vision, natural language processing and time-series analysis, and to research on model complexity and regularization.
Percentage of correct predictions; important for classification tasks.
99th percentile of inference response time; critical for production requirements.
Compute effort or monetary cost per inference; relevant for scaling.
Deep residual network that uses skip connections to enable stability in very deep architectures.
Self-attention based architecture for sequence tasks that allows parallel training.
Recurrent architecture with memory cells suitable for long-term dependencies in sequences.
Define problem and metrics; identify suitable datasets.
Evaluate architecture options (e.g. CNN, RNN, Transformer) and train proof-of-concept.
Conduct hyperparameter tuning, regularization and validation.
Set up deployment and monitoring pipeline; define retraining strategy.