Parameter

As explained in Optimization Algorithms: AI techniques for design, planning, and control problems

A fixed input value that specifies an optimization problem without being selected as a decision variable.

How Optimization Algorithms: AI techniques for design, planning, and control problems covers it

How deep it goes

Defines parameters through a ticket-pricing objective and distinguishes them from decision variables and boundary constraints.

Examples

Uses fixed costs, variable costs, ticket price, demand, and profit in an event-pricing formulation.

What it assumes

Assumes familiarity with objective functions and decision variables while defining the parameter distinction through a concrete pricing problem.

What you can do after

Evaluate optimization formulations by identifying fixed inputs, decision variables, objectives, and variable bounds.

How it connects

Connects parameters with objective functions, decision variables, boundary constraints, and algorithm parameters.

liveBookmanning.com

Read Optimization Algorithms: AI techniques for design, planning, and control problems on liveBook Get the book at manning.com

Also explained in

• 

  liveBookmanning.com
  Grokking Machine LearningA numerical model parameter that multiplies a corresponding feature and determines its contribution to the prediction.
• 

  liveBookmanning.com
  Deep Learning with PyTorch, Second EditionA learnable numeric value in a model.
• 

  liveBookmanning.com
  Natural Language Processing in Action, Second EditionA weight is a learned numerical parameter that controls the contribution of an input or connection in a neural network.
• 

  liveBookmanning.com
  Math and Architectures of Deep LearningThe tunable values of a model estimated from data.
• 

  liveBookmanning.com
  Grokking AI Algorithms, Second EditionA neural network weight is a learned numerical parameter that determines the strength of a connection or contribution between network units.
• 

  liveBookmanning.com
  Outlier Detection in PythonA configurable value that controls the behavior or fitting of a model or algorithm.
• 

  liveBookmanning.com
  Causal AIA weight parameter is a learned numerical coefficient that controls the contribution of an input in a machine-learning function or model.
• 

  liveBookmanning.com
  Learn Go with Pocket-Sized ProjectsA query parameter is an optional key-value component of a URL that supplies filtering or other request options to an API.
• 

  liveBookmanning.com
  Deep Learning with R, Third EditionA weight is a learned numerical parameter that determines part of a neural-network transformation, including an embedding lookup.
• 

  liveBookmanning.com
  Learn Generative AI with PyTorchThe learned numerical parameters of a neural network model after or during training.
• 

  liveBookmanning.com
  .NET in Action, Second EditionA query parameter is a key-value input appended to a URL that supplies options or filters for a web request.
• 

  liveBookmanning.com
  Deep Learning with JAXThe model parameters stored in a training state and updated by an optimizer during training.
• 

  liveBookmanning.com
  Prompt Engineering in PracticeA model parameter is a setting that controls a model's behavior when generating an output.
• 

  liveBookmanning.com
  Data Analysis with LLMsA model parameter is a learned numerical value that determines a machine-learning model's behavior.
• 

  liveBookmanning.com
  The Well-Grounded Python Developer: How the pros use Python and FlaskA query string is the portion of a URL containing parameters appended after a question mark.
• 

  liveBookmanning.com
  Inside Deep Learning: Math, Algorithms, ModelsA numerical value that controls a model's computation and may be selected or updated during learning.
• 

  liveBookmanning.com
  Build Python Web Apps with StreamlitA query parameter is a named key-value item in a URL query string that conveys request information or application state.
• 

  liveBookmanning.com
  Software Testing with Generative AIA learned numerical value that contributes to a model's statistical computations.
• 

  liveBookmanning.com
  Build a Large Language Model (From Scratch)An adjustable value in a neural network that is learned during training.
• 

  liveBookmanning.com
  Deep Learning with Python, Third EditionA model parameter that is updated by backpropagation during training.
• 

  liveBookmanning.com
  Distributed Machine Learning PatternsA model parameter is a learned numerical value in a machine learning model that determines the model's behavior.
• 

  liveBookmanning.com
  Sutskever's ListA learned numerical value in a model, such as a weight or bias, that is adjusted during training.
• 

  liveBookmanning.com
  Transformers in ActionA model parameter is a learned numerical value within a model that is optimized from training data.
• 

  liveBookmanning.com
  The AI Pocket BookA learned model value optimized during training rather than fixed by hand.
• 

  liveBookmanning.com
  Machine Learning Algorithms in DepthA weight matrix is a matrix of learned coefficients that parameterizes connections between neural-network units.
• 

  liveBookmanning.com
  Build a Text-to-Image Generator (from Scratch)A model parameter is a numerical value used by a model that is typically learned or adjusted during training.
• 

  liveBookmanning.com
  LLMs in ProductionA model parameter is a learned numerical value that determines part of a machine-learning model's computation.
• 

  liveBookmanning.com
  Introduction to Generative AI, Second EditionA model parameter is a learned numerical value that determines how a machine-learning model transforms inputs, with parameter count indicating model scale.
• 

  liveBookmanning.com
  The Complete Obsolete Guide to Generative AIA model parameter is a learned numerical value that determines part of a machine-learning model's behavior.
• 

  liveBookmanning.com
  Designing Deep Learning Systems: A guide for software engineersA numerical value in a model that is adjusted during training to determine its behavior.

Compare how 31 books explain parameter →

Related concepts

• hyperparametercontrasts with · A hyperparameter is a setting chosen outside ordinary parameter learning that controls a model, algorithm, or learning procedure.
• gradient descentused by · Gradient descent is an iterative optimization method that updates parameters opposite the gradient of an objective function to reduce its value.
• optional function argumentincludes · An optional function argument is an input corresponding to a parameter that a caller is not required to supply because omission is permitted by the function's interface.
• optimizationconfigured by · Optimization is the systematic selection or adjustment of values, actions, or system designs to improve a specified objective, often subject to constraints.
• fixed parameterincludes · A fixed parameter is an input with a predetermined value that cannot be changed by the automation initiator.
• prompt templateused by · A prompt template is a reusable structure for organizing instructions, context, and variable inputs supplied to a language model.

This explainer was generated by Manning's knowledge graph from the text of Optimization Algorithms: AI techniques for design, planning, and control problems. The section links point to the parts of the book it draws on.