For most tasks, you can control the training algorithm details using the trainingOptions and trainnet functions. If the trainingOptions function does not provide the options you need for your task (for example, a custom solver), then you can define your own custom training loop. Define ...
(2020), a short diagnostic tool has been developed that exploits an advanced feature selection approach with an iterative algorithm to distinguish epilepsy and psychogenic non-epileptic seizures. With the advent of natural language processing (NLP) models and the spread of pre-trained models trained ...
To train a deep learning model with a custom training loop, you can minimize the loss using gradient-descent based methods. For example, you can iteratively update the learnable parameters of the model such that it minimizes the loss. For example, you can update the learnable parameters using...