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Deep learning has been quite successfully utilized for various computer vision tasks such as object recognition and identification using different CNN architectures.
Transfer learning examples. For example a person who knows to drive a moped can easily learn to drive a scooter. Types of Transfer of Learning. 3212017 Deep learning models excel at learning from a large number of labeled examples but typically do not generalize to conditions not seen during training.
We can leverage our previous knowledge and apply it to learn a new skill. 742020 We follow an example but we can run with different approaches that we will discuss. ImageNet which contains 12 million images with 1000 categories and then use the ConvNet either as an initialization or a fixed feature extractor for the task of interest.
For instance features from a model that has learned to identify racoons may be useful to kick-start a model meant to identify tanukis. 6162019 In transfer learning the knowledge of an already trained machine learning model is applied to a different but related problem. See the notebook here.
A pre-trained model is a saved network that was previously trained on a large dataset typically on a large-scale image-classification task. A domain DD consists of a feature space XX and a marginal probability distribution P XP X over the feature space. Another type of design methodology is for example active learning.
7272018 Transfer learning involves the concepts of a domain and a task. Run in Google Colab. An out of town adventure.
So a formal definition would be. Takes you to a large city via airplane. Such transfer occurs when learning of one set of material influences the learning of another set of material later.
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