Difference between revisions of "Optimizer"
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* [[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://videos.h2o.ai/watch/4Qx2eUbrsUCZ4rThjtVxeb Driverless AI - Intro + Interactive Hands-on Lab - Video] | + | * [http://videos.h2o.ai/watch/4Qx2eUbrsUCZ4rThjtVxeb 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 options for optimizer in Tensorflow. Optimizers are the tool to minimise loss between prediction and real value. | ||
Revision as of 00:39, 28 September 2018
- TensorFlow Training Classes Python API
- Gradient Descent Optimization & Challenges
- Objective vs. Cost vs. Loss vs. Error Function
- 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.
Genetic Algorithm Optimization
