Learn more about artificialintelligence with this collection
Understanding machine learning models
Improving data analysis and decision-making
How Google uses logic in machine learning
The biggest problem, thoug h, is that models like this one are performed only on a single task. Future tasks require a new set of data points as well as equal or more amount of resources.
Transfer learning is an approach in deep learning (and machine learning) where knowledge is transferred from one model to another.
Deep learning models require a LOT of data for solving a task effectively. However, it is not often the case that so much data is available. For example, a company may wish to build a very specific spam filter to its internal communication system but does not possess lots of labelled data.
What you can do is using a pre-trained image classifier on dog photos to predict cat photos.
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Keep reading, keep studying, the more you learn the more you change. If you are doing the Python lessons please join this discord channel https://discord.gg/kugXx9KY but please follow the rules
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Transfer learning consists of taking features learned on one problem, and leveraging them on a new, similar problem. For instance, features from a model that has learned to identify racoons may be useful to kick-start a model meant to identify tanukis.
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