India is seeing strong demand for premium fruits, vegetables, flowers, and spices as urban incomes rise and supply chains modernize. Farmers who choose smart...
Recommender system techniques are methods that learn from user behavior, item attributes, and contextual cues to suggest what a person may want next. They...
Graph machine learning methods learn from data structured as nodes and edges, so models reason about relationships rather than isolated rows. These approaches encode...
Curriculum learning strategies for stable training are methods that order data, tasks, and model challenges so learning progresses from easier to harder in a...
Few-shot and low-data learning techniques are methods that help models perform well when only a handful of labeled examples are available. These approaches reduce...
Transfer learning strategies are practical ways to reuse a pretrained model’s knowledge for a new problem with less data and faster training. By starting...
Data augmentation ideas for vision and text are practical methods to expand training data without collecting new samples. They help models generalize, resist overfitting,...
Interpretable machine learning builds models and workflows that help people understand what drives predictions, validate assumptions, and diagnose failures. It supports trust, compliance, safety,...
Model calibration aligns a model’s predicted probabilities with real-world frequencies, so that a 0.8 confidence truly means about eight correct in ten. Uncertainty estimation...