MindMap Gallery Common types and applications of neural network models
Common neural network models and their applications, such as the perceptron (P), the perceptron model is also called a single-layer neural network. This neural network only contains two layers: the input layer and the output layer. There may be errors, please refer to it carefully.
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This infographic, created using EdrawMax, outlines the pivotal moments in African American history from 1619 to the present. It highlights significant events such as emancipation, key civil rights legislation, and notable achievements that have shaped the social and political landscape. The timeline serves as a visual representation of the struggle for equality and justice, emphasizing the resilience and contributions of African Americans throughout history.
This infographic, designed with EdrawMax, presents a detailed timeline of the evolution of voting rights and citizenship in the U.S. from 1870 to the present. It highlights key legislative milestones, court decisions, and societal changes that have expanded or challenged voting access. The timeline underscores the ongoing struggle for equality and the continuous efforts to secure voting rights for all citizens, reflecting the dynamic nature of democracy in America.
This infographic, created using EdrawMax, highlights the rich cultural heritage and outstanding contributions of African Americans. It covers key areas such as STEM innovations, literature and thought, global influence of music and arts, and historical preservation. The document showcases influential figures and institutions that have played pivotal roles in shaping science, medicine, literature, and public memory, underscoring the integral role of African American contributions to society.
Common neural network models Types and their applications
1||| Perceptron(P)
introduce
structure
application
1||| Classification
2||| Encoding database (multilayer perceptron)
3||| Monitor access data (multilayer perceptron)
subtopic
2||| Feed Forward (FF)
introduce
structure
application
1||| data compression
2||| pattern recognition
3||| computer vision
4||| Sonar target recognition
5||| Speech Recognition
6||| Handwritten character recognition
3||| Radial Basis Network (RBN)
introduce
structure
application
1||| function approximation
2||| time series forecasting
3||| Classification
4||| System control
4||| Deep Feed-forward (DFF)
introduce
structure
application
1||| data compression
2||| pattern recognition
3||| computer vision
4||| ECG noise filtering
5||| financial forecast
5||| Self-organizing feature map SOM (Self-organizing feature Map)
introduce
structure
application
1||| Implement data visualization
2||| medical image processing
3||| Meteorological changes
4||| traffic jam
6||| Recurrent Neural Network (RNN)
introduce
structure
application
1||| machine translation
2||| Robot control
3||| time series forecasting
4||| Speech Recognition
5||| speech synthesis
6||| Time series anomaly detection
7||| Rhythm Learning
8||| Music creation
7||| Long / Short Term Memory (LSTM)
introduce
structure
application
1||| Speech Recognition
2||| writing recognition
8||| Gated Recurrent Unit (GRU)
introduce
structure
application
1||| polyphonic music model
2||| Speech signal modeling
3||| natural language processing
9||| Auto Encoder (AE)
introduce
structure
application
1||| Classification
2||| clustering
3||| Feature compression
10||| Variational Autoencoder (VAE)
introduce
structure
application
insert between sentences
Image automatically generated
11||| Denoising Autoencoder (DAE)
introduce
structure
application
1||| Feature extraction
2||| Dimensionality reduction
12||| Sparse Autoencoder (SAE)
introduce
structure
application
1||| Feature extraction
2||| Handwritten digit recognition
13||| Markov Chain (MC)
introduce
structure
application
1||| Speech Recognition
2||| information and communication systems
3||| queuing theory
4||| statistics
14||| Hopfield Network (HN)
introduce
structure
application
1||| Optimization
2||| Image detection and recognition
3||| Medical image recognition
4||| Enhance X-ray images
15||| Boltzmann Machine (BM)
introduce
structure
application
1||| Dimensionality reduction
2||| Classification
3||| return
4||| Collaborative filtering
5||| Feature learning
16||| Restricted Boltzmann Machine (RBM)
introduce
structure
application
1||| filter
2||| Feature learning
3||| Classification
4||| Risk detection
5||| business and economic analysis
17||| Deep Belief Network (DBN)
introduce
structure
application
1||| Retrieve files/images
2||| Nonlinear dimensionality reduction
18||| Deep Convolutional Network (DCN)
introduce
structure
application
1||| Recognize faces, street signs, tumors
2||| Image Identification
3||| Video analysis
4||| natural language processing
5||| abnormal detection
6||| drug discovery
7||| checkers game
8||| time series forecasting
19||| Deconvolutional Neural Networks (DN)
introduce
structure
application
1||| Image super-resolution
2||| Surface depth estimation of images
3||| Optical flow estimation
20||| Deep Convolutional Inverse Graphics Network (DC-IGN)
introduce
structure
application
1||| Face processing
21||| Generative Adversarial Network (GAN)
introduce
structure
application
1||| Create new human poses
2||| Turn photos into Emoji
3||| facial aging
4||| super resolution
5||| clothing change
6||| Video prediction
22||| Liquid State Machine (LSM)
introduce
structure
application
Speech Recognition
computer vision
23||| Extreme Learning Machine (ELM)
introduce
structure
application
1||| Classification
2||| return
3||| clustering
4||| sparse approximation
5||| Feature learning
24||| Echo State Network (ESN)
introduce
structure
application
1||| time series forecasting
2||| data mining
25||| Deep Residual Network (DRN)
introduce
structure
application
1||| Image classification
2||| Target Detection
3||| Semantic segmentation
4||| Speech Recognition
5||| Language recognition
26||| Kohonen Networks (KN)
introduce
structure
application
1||| Dimensionality reduction
2||| Water quality assessment and prediction
3||| coastal water resources management
27||| Support Vector Machines (SVM)
introduce
structure
application
1||| Face Detection
2||| Text Categorization
3||| Classification
4||| bioinformatics
5||| handwriting recognition
28||| Neural Turing Machine (NTM)
introduce
structure
application
1||| robot
2||| Making artificial brains