Neural Network Model (NNet) Inference Task
A Neural Network Model (NNet) Inference Task is a ML model inference task designed for a neural network model to make predictions or decisions based on new, unseen data.
- Context:
- It can be solved by a Neural Network Inference System (that implements a neural network inference algorithm).
- It can range from being a Shallow Neural Network Inference Task to being a Deep Neural Network Inference Task.
- It involves processing input data through one or more layers of neurons, each designed to identify and extract patterns and features from the data.
- It can support applications requiring unstructured data prediction]]s, such as autonomous driving, medical diagnosis, and voice recognition.
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- Example(s):
- CNN Inference Tasks, such as: ...
- DNN Inference Tasks, such as: ...
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- Counter-Example(s):
- Neural Network Training Task.
- Traditional machine learning inference tasks, such as Decision Tree Inference and Logistic Regression Inference.
- See: NNet Training, Model Optimization, Hardware Acceleration, Real-Time Inference.