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Yapay ZekâUzmanlıkAIML-LM-029

Synthetic Data and AI Feedback

Models generating training data and critiques for other models, and the risks of feedback loops.

Bu konunun henüz Türkçe çevirisi yok, bu yüzden İngilizce gösteriliyor.

Zorluk
5 / 5
Önem
3 / 5
Çalışma süresi
1–2 sa
Çalışma türü
Karma

Neden önemli

The learner can describe a synthetic-data or AI-feedback pipeline and name two failure modes such as model collapse or bias amplification.

Bu konuya giden rota

Rota AIML-LM-029
What Is a Model? Tasks: Regression, Classification, and Beyond Features and Targets Classification Framing Linear Regression I: Prediction and Residuals Embeddings Tokens and Tokenization Scores and Logits Loss and Fitting: Squared Error Sigmoid and Probabilities Multiclass Scores and Softmax The Artificial Neuron Autoregression Objective Functions and Empirical Risk Logistic Regression Activation Functions Next-Token Prediction Gradient Descent in Machine Learning Likelihood The Multilayer Perceptron Binary Cross-Entropy Cross-Entropy Loss Stochastic and Mini-batch Optimization Transfer Learning The Language-Modeling Objective Fine-Tuning Pretraining Supervised Fine-Tuning Preference Optimization Reward Models from Human Preferences Synthetic Data and AI Feedback