Representation learning is a very important aspect of machine learning which automatically discovers the feature patterns in the data. When the machine is provided with the data, it learns the representation itself without any human intervention. The goal of representation learning is to train machine learning algorithms to learn useful representations, such as those that are interpretable, incorporate latent features, or can be used for transfer learning. In this article, we will discuss the concept of representation learning along with its need and different approaches. The major points to be covered in this article are listed below.
- Need of Representation Learning
- What is Representation Learning?
- Methods of Representation Learning
- Supervised Methods
- Unsupervised Methods
- Deep Architectures Methods
Let’s start the discussion by understanding what is the actual need for representation learning.