@boscokat: Different AI problems need different learning strategies. Here's a quick breakdown: 🔹 In‑Context Learning – No weight changes. Fast setup with few‑shot prompting. Best for prompt‑based tasks. 🔹 Fine‑Tuning – Trains a base model on task‑specific data. Better consistency and domain performance. 🔹 Transfer Learning – Reuses pretrained knowledge on related tasks. Saves time and data. 🔹 Reinforcement Learning – Learns through actions + rewards. Best for robotics and recommendations. 🔹 Continual Learning – Updates as new data arrives while retaining old knowledge. Best for evolving environments. 🔹 Federated Learning – Trains across distributed devices without centralising raw data. Privacy‑friendly and collaborative. 🔹 Active Learning – Picks the most useful unlabeled examples for human review. Reduces annotation cost. 🔹 Meta‑Learning – Helps models adapt quickly to new tasks with limited data. "Learn to learn." Choose the method that fits your problem, data, and business goal. 🎯 #NamibiaAI #MachineLearning #AItraining #DeepLearning #UNAM
