Today we continue Machine Learning domain post, focus on Benefits of Transfer Learning to Deep Learning. Transfer Learning (TL) for Deep Learning (DL) is a machine learning (ML) approach and techniques used to save time and resources from having to train multiple machine learning models from scratch to complete similar tasks, in particular for tasks such as image categorisation or natural language processing. It is particularly useful to deal with new domains where we have insufficient data where we can use a big pre-existing data pool that can be transferred to the problem set, which speeds up the process for working out the useful modelling.
The most important benefits of transfer learning is it can be applied on an enormous bigger training set that could be get for the target task, this is why it called “Transfer learning”. Transfer learning can be handle where the task model origin already competent. Basically when using the Transfer Learning correctly it can give higher starting accuracy, faster convergence and higher asymptotic accuracy to which the training gathers.
Machine learning (ML) in overall, include the sub domain of deep learning, transfer learning if use together with the big data and artificial intelligence together, it provide a very clear direction for next wave for the productivity breakthrough such as smart robotics involvement into the modern and future production, research and development, where in the past is highly knowledge workers intensive. If the transfer learning worked out modeling really so accurately and can be transferred to study for different domains, one of the immediate benefits is to shorten the research and development for a lot of science related fields, which result in speedy technology breakthrough for the near future faster than ever. This is also one of the reasons one class of the rising job category is known as data scientist.
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