Browse all content tagged with Transfer Learning
Data scarcity refers to insufficient data for training machine learning models or comprehensive analysis, hindering the development of accurate AI systems. Discover causes, impacts, and techniques to overcome data scarcity in AI and automation.
AI Extensibility refers to the ability of AI systems to expand their capabilities to new domains, tasks, and datasets without major retraining, using techniques like transfer learning, multi-task learning, and modular design for flexibility and seamless integration.
Few-Shot Learning is a machine learning approach that enables models to make accurate predictions using only a small number of labeled examples. Unlike traditional supervised methods, it focuses on generalizing from limited data, leveraging techniques like meta-learning, transfer learning, and data augmentation.
Model fine-tuning adapts pre-trained models for new tasks by making minor adjustments, reducing data and resource needs. Learn how fine-tuning leverages transfer learning, different techniques, best practices, and evaluation metrics to efficiently improve model performance in NLP, computer vision, and more.
Transfer learning is a sophisticated machine learning technique that enables models trained on one task to be reused for a related task, improving efficiency and performance, especially when data is scarce.
Transfer Learning is a powerful AI/ML technique that adapts pre-trained models to new tasks, improving performance with limited data and enhancing efficiency across various applications like image recognition and NLP.
Fastai is a deep learning library built on PyTorch, offering high-level APIs, transfer learning, and a layered architecture to simplify neural network development for vision, NLP, tabular data, and more. Developed by Jeremy Howard and Rachel Thomas, Fastai is open-source and community-driven, making state-of-the-art AI accessible for everyone.
Zero-Shot Learning is a method in AI where a model recognizes objects or data categories without having been explicitly trained on those categories, using semantic descriptions or attributes to make inferences. It's especially useful when collecting training data is challenging or impossible.
