![]() However, think about everything our model for recognizing trucks could use from the model that was originally Now, of course, if the two tasks are different, then there will be some information that the model has learned that may not apply to our new task, or there may be new information that the model needs to learn from the data regarding the new task thatįor example, a model trained on cars is not going to have ever seen a truck bed, so this feature is something new the model would have to learn about. If we can find a trained model that already does one task well, and that task is similar to ours in at least some remote way, then we can take advantage of everything the model has already learned ![]() This is what makes the fine-tuning approach so attractive. When building a model from scratch, we usually must try many approaches through trial-and-error.įor example, we have to choose how many layers we're using, what types of layers we're using, what order to put the layers in, how many nodes to include in each layer, decide how much regularization to use, what to set our learning rateīuilding and validating our model can be a huge task in its own right, depending on what data we're training it on. Model to make it perform a second similar task.Īssuming the original task is similar to the new task, using an artificial neural network that has already been designed and trained allows us to take advantage of what the model has already learned without having to develop it from scratch. ![]() ![]() Specifically, fine-tuning is a process that takes a model that has already been trained for one given task and then tunes or tweaks the Our solution takes expense management a step further where we truly optimize our clients’ programs, given their very specific needs and circumstances.Transfer learning occurs when we use knowledge that was gained from solving one problem and apply it to a new but related problem.įor example, knowledge gained from learning to recognize cars could be applied in a problem of recognizing trucks.įine-tuning is a way of applying or utilizing transfer learning. “Ultimately eMOAT SM, operated by world-class experts, is the engine that powers us to achieve a level of expense management not available elsewhere in the marketplace, and allows us to achieve results way beyond expert contract negotiation and contract enforcement. “eMOAT SM not only allows us to defend our clients with highly sophisticated and targeted technology but it frees up our world-class experts to then go on the offense for our clients,” said Matt Smith, Executive Vice President & Chief Financial Officer, Fine Tune. Over the years, however, we’ve learned that in order to effectively manage and truly optimize our clients’ expenses, we needed to go way beyond invoice-to-contract compliance auditing.” “So, when we built it, the main function was to audit for contract compliance. “eMOAT SM was initially launched as an auditing application to ensure our clients, across many industries, actually got what they signed up for,” said Ben Miller, Chief Information Officer, Fine Tune. For black-and-white compliance, we run a range of additional expense management reports each month to ensure that target spend levels are attained.
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