Difference between revisions of "Optimizer"
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* [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Optimizer]] ... [[Train, Validate, and Test]] | * [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Optimizer]] ... [[Train, Validate, and Test]] | ||
* [[Loss]] Functions | * [[Loss]] Functions | ||
| + | * [[Activation Functions]] | ||
* [[Gradient Descent Optimization & Challenges]] | * [[Gradient Descent Optimization & Challenges]] | ||
* [[Objective vs. Cost vs. Loss vs. Error Function]] | * [[Objective vs. Cost vs. Loss vs. Error Function]] | ||
Revision as of 20:17, 10 July 2023
YouTube search... ...Google search
- AI Solver ... Algorithms ... Administration ... Model Search ... Discriminative vs. Generative ... Optimizer ... Train, Validate, and Test
- Loss Functions
- Activation Functions
- Gradient Descent Optimization & Challenges
- Objective vs. Cost vs. Loss vs. Error Function
- TensorFlow Training Classes Python API
- H2O Driverless AI - Intro + Interactive Hands-on Lab - Video
There are many options for optimizer in TensorFlow. Optimizers are the tool to minimise loss between prediction and real value. There are many different weights a model could learn, and brute-force testing every one would take forever. Instead, an optimizer is chosen which evaluates the loss value, and smartly updates the weights. Click here For a list of Keras optimizer functions. Optimizer is one of the two parameters required to compile a model...
Genetic Algorithm Optimization
