Difference between revisions of "Optimizer"
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* [[Loss]] Functions | * [[Loss]] Functions | ||
| − | * [ | + | * [[Model Search]] |
* [[Gradient Descent Optimization & Challenges]] | * [[Gradient Descent Optimization & Challenges]] | ||
* [[Objective vs. Cost vs. Loss vs. Error Function]] | * [[Objective vs. Cost vs. Loss vs. Error Function]] | ||
| + | * [http://www.tensorflow.org/api_guides/python/train TensorFlow Training Classes Python API] | ||
* [http://videos.h2o.ai/watch/4Qx2eUbrsUCZ4rThjtVxeb H2O Driverless AI - Intro + Interactive Hands-on Lab - Video] | * [http://videos.h2o.ai/watch/4Qx2eUbrsUCZ4rThjtVxeb H2O Driverless AI - Intro + Interactive Hands-on Lab - Video] | ||
Revision as of 20:15, 6 March 2022
YouTube search... ...Google search
- Loss Functions
- Model Search
- 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
