Machine Learning

Reinforcement Learning involves training agents to make decisions in an environment to maximize a cumulative reward. It is based on the concept of learning from interactions and feedback, where the agent takes actions, receives feedback, and adjusts its strategy to achieve optimal performance.

Deep Learning is a subfield of machine learning that employs neural networks with multiple layers (deep neural networks) to model complex patterns and representations in data.

Few-shot Learning is a machine learning paradigm where models are trained to make accurate predictions or decisions with very limited examples.

Semi-supervised Learning is an approach that combines both labeled and unlabeled data during training. The model leverages the limited labeled data and the vast amount of unlabeled data to improve its performance on various tasks.

Representation Learning focuses on learning meaningful and informative representations of data. It involves training models to automatically discover features or embeddings that capture essential characteristics of the input data, improving the performance of downstream tasks.

Data Augmentation is a technique used to artificially expand the size of a dataset by applying various transformations to the existing data. This helps improve model generalization and robustness by exposing it to diverse input variations.