AI Training

Understand how AI systems learn from data. From training data preparation to transfer learning—practical foundations for understanding how AI models are built and refined. Essential background for evaluating AI systems and understanding their capabilities.

AI Training Data Basics: What AI Learns From

Beginner

Understand how training data shapes AI behavior. From data collection to quality—what you need to know about the foundation of all AI systems.

9 min read

training datadata qualitymachine learning

Data Labeling Fundamentals: Creating Quality Training Data

Intermediate

Learn the essentials of data labeling for AI. From annotation strategies to quality control—practical guidance for creating the labeled data that AI needs to learn.

10 min read

data labelingannotationtraining data

Transfer Learning Explained: Building on What AI Already Knows

Intermediate

Understand transfer learning and why it matters. Learn how pre-trained models accelerate AI development and reduce data requirements.

9 min read

transfer learningpre-trained modelsfine-tuning

Preference Optimization: DPO and Beyond

Advanced

Direct Preference Optimization (DPO) and variants train models on human preferences without separate reward models. Simpler, more stable than RLHF.

7 min read

DPORLHFpreference learning

Training Efficient Models: Doing More with Less

Advanced

Learn techniques for training AI models efficiently. From data efficiency to compute optimization—practical approaches for reducing training costs and time.

10 min read

efficiencytrainingoptimization