Difference between revisions of "Optimizer"
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* [http://www.tensorflow.org/api_guides/python/train TensorFlow Training Classes Python API] | * [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] | ||
| + | * [[Process Supervision]] | ||
There are many options for optimizer in [[TensorFlow]]. Optimizers are the tool to minimise [[loss]] between prediction and real value. There are many different [[Activation Functions#Weights|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 [[Activation Functions#Weights|weights]]. [http://keras.io/optimizers/ Click here For a list of Keras optimizer functions.] Optimizer is one of the two parameters required to compile a model... | There are many options for optimizer in [[TensorFlow]]. Optimizers are the tool to minimise [[loss]] between prediction and real value. There are many different [[Activation Functions#Weights|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 [[Activation Functions#Weights|weights]]. [http://keras.io/optimizers/ Click here For a list of Keras optimizer functions.] Optimizer is one of the two parameters required to compile a model... | ||
Revision as of 21:44, 28 November 2023
YouTube search... ...Google search
- AI Solver ... Algorithms ... Administration ... Model Search ... Discriminative vs. Generative ... Optimizer ... Train, Validate, and Test
- Backpropagation ... FFNN ... Forward-Forward ... Activation Functions ...Softmax ... Loss ... Boosting ... Gradient Descent ... Hyperparameter ... Manifold Hypothesis ... PCA
- Objective vs. Cost vs. Loss vs. Error Function
- TensorFlow Training Classes Python API
- H2O Driverless AI - Intro + Interactive Hands-on Lab - Video
- Process Supervision
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
