Difference between revisions of "Softmax"
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[http://www.youtube.com/results?search_query=Softmax+Dimensional+Reduction+Algorithm Youtube search...] | [http://www.youtube.com/results?search_query=Softmax+Dimensional+Reduction+Algorithm Youtube search...] | ||
[http://www.google.com/search?q=Softmax+Dimensional+Reduction+Algorithm+deep+machine+learning+ML+artificial+intelligence ...Google search] | [http://www.google.com/search?q=Softmax+Dimensional+Reduction+Algorithm+deep+machine+learning+ML+artificial+intelligence ...Google search] | ||
| − | * [[Dimensional Reduction]] | + | * [[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]] |
| + | * [[Embedding]] ... [[Fine-tuning]] ... [[Retrieval-Augmented Generation (RAG)|RAG]] ... [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]]. [[...find outliers]] | ||
* [[Pooling / Sub-sampling: Max, Mean]] | * [[Pooling / Sub-sampling: Max, Mean]] | ||
* [[(Deep) Convolutional Neural Network (DCNN/CNN)]] | * [[(Deep) Convolutional Neural Network (DCNN/CNN)]] | ||
| − | * [[Activation Functions]] | + | * [[Backpropagation]] ... [[Feed Forward Neural Network (FF or FFNN)|FFNN]] ... [[Forward-Forward]] ... [[Activation Functions]] ... [[Loss]] ... [[Boosting]] ... [[Gradient Descent Optimization & Challenges|Gradient Descent]] ... [[Algorithm Administration#Hyperparameter|Hyperparameter]] ... [[Manifold Hypothesis]] ... [[Principal Component Analysis (PCA)|PCA]] |
| + | * [[Analytics]] ... [[Visualization]] ... [[Graphical Tools for Modeling AI Components|Graphical Tools]] ... [[Diagrams for Business Analysis|Diagrams]] & [[Generative AI for Business Analysis|Business Analysis]] ... [[Requirements Management|Requirements]] ... [[Loop]] ... [[Bayes]] ... [[Network Pattern]] | ||
* [http://developers.google.com/machine-learning/crash-course/multi-class-neural-networks/softmax Multi-Class Neural Networks: Softmax] | * [http://developers.google.com/machine-learning/crash-course/multi-class-neural-networks/softmax Multi-Class Neural Networks: Softmax] | ||
* [http://towardsdatascience.com/softmax-function-simplified-714068bf8156 The Softmax Function, Simplified - How a regression formula improves accuracy of deep learning models | Hamza Mahmood - Towards Data Science] | * [http://towardsdatascience.com/softmax-function-simplified-714068bf8156 The Softmax Function, Simplified - How a regression formula improves accuracy of deep learning models | Hamza Mahmood - Towards Data Science] | ||
* [http://dataaspirant.com/2017/03/07/difference-between-softmax-function-and-sigmoid-function/ Difference Between Softmax Function And Sigmoid Function | Saimadhu Polamuri - Dataaspirant] | * [http://dataaspirant.com/2017/03/07/difference-between-softmax-function-and-sigmoid-function/ Difference Between Softmax Function And Sigmoid Function | Saimadhu Polamuri - Dataaspirant] | ||
| − | Function is used in various multiclass classification methods, such as multinomial logistic regression (also known as softmax regression), multiclass linear discriminant analysis, [[Bayes#Naive Bayes|Naive Bayes]] classifiers, and artificial neural networks. | + | |
| + | <b>Softmax Function</b> is a widely used mathematical function in artificial intelligence (AI), particularly in classification tasks. Function is used in various multiclass classification methods, such as multinomial logistic regression (also known as softmax regression), multiclass linear discriminant analysis, [[Bayes#Naive Bayes|Naive Bayes]] classifiers, and artificial [[Neural Network]]s. It is used to convert a vector of real numbers into a probability distribution over multiple classes. The Softmax function takes as input a vector of values, often referred to as logits or scores, and transforms them into a probability distribution where the sum of all probabilities is equal to 1. | ||
| + | |||
| + | The softmax function is defined as follows: | ||
| + | |||
| + | * The input to the softmax function is a vector of scores or logits, denoted as z = [z1, z2, ..., zn], where n is the number of classes. | ||
| + | * Each element in the input vector represents the score or the strength of association for a specific class. | ||
| + | * The softmax function calculates the exponentiation of each element in the input vector, which ensures that the resulting values are positive. | ||
| + | * The exponentiated values are then normalized by dividing each element by the sum of all exponentiated values. | ||
| + | |||
| + | The formula for the softmax function can be expressed as: | ||
| + | |||
| + | * softmax(z) = [e^z1 / (e^z1 + e^z2 + ... + e^zn), e^z2 / (e^z1 + e^z2 + ... + e^zn), ..., e^zn / (e^z1 + e^z2 + ... + e^zn)] | ||
| + | |||
| + | The Softmax function can be interpreted as providing a probability-like output for each class, where higher scores in the input vector correspond to higher probabilities. By normalizing the exponentiated values, the softmax function emphasizes the larger values and suppresses the smaller ones. | ||
| + | |||
| + | The softmax function is commonly used in the final layer of a neural network for multi-class classification problems. The output of the softmax function can be interpreted as the predicted probabilities of each class. The class with the highest probability is then selected as the predicted class label. | ||
| + | |||
| + | The softmax function has several desirable properties that make it suitable for classification tasks. It ensures that the predicted probabilities are non-negative and sum up to 1, making them interpretable as probabilities. Additionally, the softmax function is differentiable, which allows for efficient training of neural networks using gradient-based optimization algorithms. | ||
| + | |||
| + | In summary, the softmax function is a crucial component in AI models for multi-class classification tasks. It transforms a vector of scores or logits into a probability distribution, enabling the prediction of class labels based on the calculated probabilities. | ||
http://i1.wp.com/dataaspirant.com/wp-content/uploads/2017/03/Softmax_Graph.png | http://i1.wp.com/dataaspirant.com/wp-content/uploads/2017/03/Softmax_Graph.png | ||
Latest revision as of 11:57, 13 September 2023
Youtube search... ...Google search
- Backpropagation ... FFNN ... Forward-Forward ... Activation Functions ...Softmax ... Loss ... Boosting ... Gradient Descent ... Hyperparameter ... Manifold Hypothesis ... PCA
- Embedding ... Fine-tuning ... RAG ... Search ... Clustering ... Recommendation ... Anomaly Detection ... Classification ... Dimensional Reduction. ...find outliers
- Pooling / Sub-sampling: Max, Mean
- (Deep) Convolutional Neural Network (DCNN/CNN)
- Backpropagation ... FFNN ... Forward-Forward ... Activation Functions ... Loss ... Boosting ... Gradient Descent ... Hyperparameter ... Manifold Hypothesis ... PCA
- Analytics ... Visualization ... Graphical Tools ... Diagrams & Business Analysis ... Requirements ... Loop ... Bayes ... Network Pattern
- Multi-Class Neural Networks: Softmax
- The Softmax Function, Simplified - How a regression formula improves accuracy of deep learning models | Hamza Mahmood - Towards Data Science
- Difference Between Softmax Function And Sigmoid Function | Saimadhu Polamuri - Dataaspirant
Softmax Function is a widely used mathematical function in artificial intelligence (AI), particularly in classification tasks. Function is used in various multiclass classification methods, such as multinomial logistic regression (also known as softmax regression), multiclass linear discriminant analysis, Naive Bayes classifiers, and artificial Neural Networks. It is used to convert a vector of real numbers into a probability distribution over multiple classes. The Softmax function takes as input a vector of values, often referred to as logits or scores, and transforms them into a probability distribution where the sum of all probabilities is equal to 1.
The softmax function is defined as follows:
- The input to the softmax function is a vector of scores or logits, denoted as z = [z1, z2, ..., zn], where n is the number of classes.
- Each element in the input vector represents the score or the strength of association for a specific class.
- The softmax function calculates the exponentiation of each element in the input vector, which ensures that the resulting values are positive.
- The exponentiated values are then normalized by dividing each element by the sum of all exponentiated values.
The formula for the softmax function can be expressed as:
- softmax(z) = [e^z1 / (e^z1 + e^z2 + ... + e^zn), e^z2 / (e^z1 + e^z2 + ... + e^zn), ..., e^zn / (e^z1 + e^z2 + ... + e^zn)]
The Softmax function can be interpreted as providing a probability-like output for each class, where higher scores in the input vector correspond to higher probabilities. By normalizing the exponentiated values, the softmax function emphasizes the larger values and suppresses the smaller ones.
The softmax function is commonly used in the final layer of a neural network for multi-class classification problems. The output of the softmax function can be interpreted as the predicted probabilities of each class. The class with the highest probability is then selected as the predicted class label.
The softmax function has several desirable properties that make it suitable for classification tasks. It ensures that the predicted probabilities are non-negative and sum up to 1, making them interpretable as probabilities. Additionally, the softmax function is differentiable, which allows for efficient training of neural networks using gradient-based optimization algorithms.
In summary, the softmax function is a crucial component in AI models for multi-class classification tasks. It transforms a vector of scores or logits into a probability distribution, enabling the prediction of class labels based on the calculated probabilities.
