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Glossary
42 terms, explained briefly and precisely — every lesson links right here whenever a technical word first appears.
A
- AI (Artificial Intelligence)Umbrella term for systems that solve tasks usually associated with perceiving, language, planning, or decision-making.
- AlgorithmA general solution procedure made of a limited sequence of clear steps — the idea behind a program, not the concrete code itself.
- ArchitectureThe blueprint of a model: what is computed with the numbers and how many parameters there are for it, regardless of their values.
B
C
E
F
G
H
I
- Image classifierA trained model that assigns images to one of several predefined categories, such as "cat" or "dog".
- InferenceUsing a fully trained model: with its fixed parameters, it computes an output from an input.
- InputThe data fed into a computational step — a number, an image, a text, or many values at once.
L
- Label (Target)The expected correct output that a model's prediction is compared against during training, also called the target.
- Language modelA model trained on large amounts of text that rates how well each text piece fits next; that is how text emerges piece by piece.
- Learning rateA hyperparameter that sets how strongly the training algorithm adjusts the parameters at each step.
M
- Machine learningThe umbrella term for methods where a training algorithm sets a model's parameters from examples, instead of a human writing decision rules by hand.
- MatrixA matrix is a table of numbers with two axes, rows and columns, in which every number has a fixed address.
- ModelA calculation whose behavior additionally depends on stored, trained numerical values (parameters).
O
P
- ParametersThe stored, adjustable numerical values a trained model consists of — also called weights.
- ProbabilityA number between 0 and 100 percent that states how often something happens if the same situation repeats very many times.
- ProgramA sequence of instructions written by humans that a computer carries out step by step; every rule is fixed before it starts.
- PromptThe text input you give an AI model — it only works while it is sent along and doesn't change the model's stored parameters.
Q
R
S
- Sample (Example)A matched pair of input and expected output that a model is trained on, also called a training example.
- SamplingChoosing the next text piece by weighted chance: each piece comes up roughly as often as its probability says.
- ScalarA scalar is a single number with no place in a list or table, the simplest block of numbers, with no axis at all.
- ScoreA numeric value a model uses to rate a possible answer, such as a class or the next piece of text, before it becomes a probability.
- SentencePieceA language-independent system for learning and applying subword tokenizers directly from raw text.
- SoftmaxThe calculation a model uses to turn a list of scores into probabilities that add up to 100 percent.
- Spam filterA trained model that classifies an email as spam or not spam, often expressed as a probability such as "92% spam".
- Subword TokenA reusable text piece that can be smaller than a word, so that rare words can be built from known parts.
T
- TemperatureA setting by which the scores are divided before softmax: low values make the selection more predictable, high values more varied.
- TensorA block of numbers with any number of axes; scalar, vector and matrix are special cases of it, and AI models compute in such blocks.
- TokenOne text unit from a tokenizer’s fixed vocabulary: a whole word, a word part, a single character, or a punctuation mark.
- Token IDThe fixed identifying number of a token in a particular tokenizer’s vocabulary, which the model works with instead of text.
- TokenizerThe fixed procedure that converts text into tokens and token IDs and assembles IDs back into text.
- Training algorithmThe fixed procedure applied to training examples that adjusts a model's parameters step by step to reduce its errors.
- Training dataThe examples a training algorithm is applied to in order to set a model's parameters, such as labeled emails or large amounts of text.
V
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