Methods and apparatus for training a neural network

At least one non-transitory computer-readable storage medium comprising instructions which, when executed, cause a processor to at least:

• determine an amount of training error experienced in a prior training epoch;
• determine a gradient descent value based on the amount of training error;
• calculate a learning rate based on the gradient descent value and a selected number of epochs such that a neural network training process is completed within the selected number of epochs;
• and
• update weighting parameters of the neural network based on the learning rate.

A method of training a neural network, the method comprising:

• determining an amount of training error experienced in a prior training epoch;
• determining a gradient descent value based on the amount of training error;
• calculating, by executing an instruction with a processor, a learning rate based on the gradient descent value, the amount of training error, and tuning parameters, the tuning parameters selected such that a training process is completed within a maximum number of epochs;
• and
• updating weighting parameters of the neural network based on the learning rate.