Knowledge Learning
- (Harvard University - Joyce Yang)
- Overview
Representation learning is a key component of deep learning. Representation learning is defined as the process of learning a representation from input data towards specific tasks such as classification, retrieval, or clustering, by extracting meaningful information to bridge the gap between low-level and higher-level semantic concepts.
Representation learning is important because raw data is often too complex for machines to process directly. By simplifying the data into understandable patterns, representation learning makes it easier for machines to perform tasks.
Here are some key aspects of representation learning:
- Interpretability: Representation learning can enhance interpretability by revealing hidden features.
- Transfer learning: Representation learning can be used for transfer learning, where representations learned for one task can be used to solve another task.
- Supervised vs. unsupervised: Representation learning can be supervised, where annotated data is used to learn representations for a task, or unsupervised, where label-free data is used to learn representations.
- Model performance: Model performance is regularly assessed, and the model evolves over time to adapt to new data and challenges.
- Neural Networks and Representation Learning
- Deep Learning vs Representation Learning
- Feature Learning vs Representation Learning
- Manifold Learning vs Representation Learning
[More to come ...]
