What Is Transfer Learning? | AI 101 - Unite.ai

When practicing machine learning, training a model can take a long time, according to Daniel Nelson, Blogger and programmer with specialties in machine learning and deep learning topics.

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Creating a model architecture from scratch, training the model, and then tweaking the model is a massive amount of time and effort. A far more efficient way to train a machine learning model is to use an architecture that has already been defined, potentially with weights that have already been calculated. This is the main idea behind transfer learning, taking a model that has already been used and repurposing it for a new task.

Before delving into the different ways that transfer learning can be used, let’s take a moment to understand why transfer learning is such a powerful and useful technique.

Solving A Deep Learning Problem 
When you are attempting to solve a deep learning problem, like building an image classifier, you have to create a model architecture and then train the model on your data. Training the model classifier involves adjusting the weights of the network, a process that can take hours or even days depending on the complexity of both the model and the dataset. The training time will scale in accordance with the size of the dataset and the complexity of the model architecture...

Transfer Learning Examples 
The most common applications of transfer learning are probably those that use image data as inputs. These are often prediction/classification tasks. The way Convolutional Neural Networks interpret image data lends itself to reusing aspects of models, as the convolutional layers often distinguish very similar features.
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Source: Unite.ai