https://machinelearningmastery.com/transfer-learning-for-deep-learning/

University of Michigan - Ann Arbor

- Developing your own model
- Using a pre-trained model

Two Approaches to Transfer Learning in Deep Learning

- Select a similar modeling problem 
- Create a complex model for the first task
- Reuse model from the first task on second task
- Tune and perfect the model



1) Developing your own model


Another approach is to use deep learning to discover the best representation of your problem, which means finding the most important features. This approach is also known as representation learning, and can often result in a much better performance than can be obtained with hand-designed representation.
In machine learning, features are usually manually hand-crafted by researchers and domain experts. Fortunately, deep learning can extract features automatically. Of course, this doesn't mean feature engineering and domain knowledge isn’t important anymore — you still have to decide which features you put into your network. That said, neural networks have the ability to learn which features are really important and which ones aren’t. A representation learning algorithm can discover a good combination of features within a very short timeframe, even for complex tasks which would otherwise require a lot of human effort.


FEATURE EXTRACTION

Pre-training in deep learning involves optimizing a neural network on an initial task before it is fine-tuned for a specific application. The core idea is that a model trained for one purpose can be successfully adapted for another. This strategy avoids the need to build complex models from the ground up, a significant advantage when dealing with tasks that have scarce labeled data. By utilizing tasks with more abundant supervision signals, pre-training reduces dependence on task-specific labels and facilitates the creation of more versatile, general-purpose models.

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