Across
- 4. A type of neural network architecture consisting of two networks – a generator and a discriminator – that compete against each other to generate realistic data
- 10. The labeled dataset used to train machine learning models, consisting of input examples paired with corresponding output labels to facilitate learning and model optimization.
- 11. A computational model inspired by the human brain's neural structure, used for pattern recognition, classification, and regression tasks in machine learning.
- 12. The ability of artificial intelligence systems to provide understandable explanations or justifications for their decisions and predictions, enhancing transparency, trust, and interpretability in AI applications.
Down
- 1. The presence of systematic errors or prejudices in machine learning models or datasets that result in unfair or discriminatory outcomes, requiring mitigation strategies to ensure equitable decision-making.
- 2. The process of assessing the performance and effectiveness of machine learning models using various metrics and techniques, such as accuracy, precision, recall, and F1 score.
- 3. A subset of machine learning techniques that utilize neural networks with multiple layers to automatically learn representations of data for feature extraction and transformation.
- 5. A machine learning paradigm where an agent learns to make sequential decisions by interacting with an environment and receiving feedback in the form of rewards or penalties.
- 6. A machine learning technique where knowledge gained from training on one task or dataset is applied to a different but related task or dataset, often used to improve model performance with limited training data.
- 7. A field of artificial intelligence focused on enabling computers to understand, interpret, and generate human language, enabling applications such as chatbots, sentiment analysis, and machine translation.
- 8. The process of using generative models to produce realistic images from input data or random noise, often employed in applications like style transfer, image synthesis, and content creation.
- 9. The practice of developing and deploying artificial intelligence systems in a responsible and ethical manner, considering factors such as fairness, transparency, accountability, and societal impact.