ML & AI Glossary

Quick reference for terms used across KiranVision courses.

A–C

Activation function — Non-linearity applied after weighted sum (ReLU, sigmoid, tanh).

Backpropagation — Algorithm to compute gradients via chain rule for training neural nets.

Bias-variance tradeoff — Balance between underfitting (high bias) and overfitting (high variance).

Classification — Predicting discrete categories.

Cross-validation — Rotating train/validation splits to estimate generalization.

D–G

Deep learning — ML using neural networks with many layers.

Embedding — Dense vector representation of discrete items (words, users).

Epoch — One full pass through the training dataset.

Feature engineering — Creating informative inputs from raw data.

Gradient descent — Iterative optimization along negative gradient of loss.

H–O

Heuristic — Estimate guiding search (e.g., A*).

Hyperparameter — Settings chosen before training (learning rate, tree depth).

LLM — Large language model trained on text at scale.

MDP — Markov decision process formalism for reinforcement learning.

Overfitting — Model fits training noise; poor test performance.

P–Z

Precision / Recall — Classification metrics for positive class quality and coverage.

Regularization — Penalties (L1/L2, dropout) to reduce overfitting.

Supervised learning — Learning from labeled examples.

Tensor — Multi-dimensional array generalizing scalars, vectors, matrices.

Transformer — Architecture using self-attention for sequences.