Difference between revisions of "Optimizer"
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[http://www.youtube.com/results?search_query=optimizers+deep+learning YouTube search...] | [http://www.youtube.com/results?search_query=optimizers+deep+learning YouTube search...] | ||
[http://www.google.com/search?q=optimizers+machine+learning+ML+artificial+intelligence ...Google search] | [http://www.google.com/search?q=optimizers+machine+learning+ML+artificial+intelligence ...Google search] | ||
| − | * [ | + | * [[Agents#AI Agent Optimization|AI Agent Optimization]] ... [[Optimization Methods]] ... [[Optimizer]] ... [[Objective vs. Cost vs. Loss vs. Error Function]] ... [[Exploration]] |
| − | + | * [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Train, Validate, and Test]] | |
| − | + | * [[Backpropagation]] ... [[Feed Forward Neural Network (FF or FFNN)|FFNN]] ... [[Forward-Forward]] ... [[Activation Functions]] ...[[Softmax]] ... [[Loss]] ... [[Boosting]] ... [[Gradient Descent Optimization & Challenges|Gradient Descent]] ... [[Algorithm Administration#Hyperparameter|Hyperparameter]] ... [[Manifold Hypothesis]] ... [[Principal Component Analysis (PCA)|PCA]] | |
| + | * [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... | ||
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| + | ---- | ||
| + | ::<code> model.compile(optimizer='[[Average-Stochastic Gradient Descent (SGD) Weight-Dropped LSTM (AWD-LSTM) |sgd]]'. [[loss]]='mean_squared_error')</code> | ||
| + | ---- | ||
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<youtube>cJA5IHIIL30</youtube> | <youtube>cJA5IHIIL30</youtube> | ||
Latest revision as of 20:30, 5 March 2024
YouTube search... ...Google search
- AI Agent Optimization ... Optimization Methods ... Optimizer ... Objective vs. Cost vs. Loss vs. Error Function ... Exploration
- AI Solver ... Algorithms ... Administration ... Model Search ... Discriminative vs. Generative ... Train, Validate, and Test
- Backpropagation ... FFNN ... Forward-Forward ... Activation Functions ...Softmax ... Loss ... Boosting ... Gradient Descent ... Hyperparameter ... Manifold Hypothesis ... PCA
- 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
