11. Representation Learning & Structured Prediction
The chapter covers representation learning, which automates the feature engineering process in machine learning, and structured prediction, which deals with interdependent outputs. It examines various models and techniques such as autoencoders, supervised learning, and conditional random fields. The integration of these paradigms enhances the performance and capability of machine learning in complex tasks across multiple domains.
Sections
Navigate through the learning materials and practice exercises.
What we have learnt
- Representation learning automates the extraction of features from raw data, improving model generalization.
- Structured prediction is essential for modeling output variables that are interrelated and requires specialized algorithms.
- The integration of representation and structured learning leads to scalable and interpretable machine learning models.
Key Concepts
- -- Representation Learning
- A set of techniques that allow a system to automatically learn features from raw data for downstream tasks.
- -- Structured Prediction
- Tasks that involve outputs that are interdependent and require specific structured models to handle their complexity.
- -- Autoencoders
- Neural networks designed to learn efficient representations of data through compression and reconstruction.
- -- Conditional Random Fields (CRFs)
- A type of statistical modeling used for predicting sequences while considering the context of neighboring variables.
- -- SelfSupervised Learning
- A learning paradigm that utilizes the data itself to generate labels or signals for training models.
Additional Learning Materials
Supplementary resources to enhance your learning experience.