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Main Types of Neural Networks and its Applications — Tutorial. A tutorial on the main types of neural networks and their applications to real-world challenges. Nowadays, there are many types of neural networks in deep learning which are used for different purposes. This article goes through the most used topologies in neural networks, briefly explains how they work, and covers some of their real-world applications. Figure 1: Main types of neural networks.
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| Towards AI Members | The Data-driven Community N owadays, there are many types of neural networks in deep learning which are used for different purposes. In this article, we will go through the most used topologies in neural networks, briefly introduce how they work, along some of their applications to real-world challenges. Figure 2: The perceptron: a probabilistic model for information storage and organization in the brain. This article is our third tutorial on neural networks; to start with our first one, check out neural networks from scratch with Python code and math in detail. Neural Network Topologies
Figure 3: Representation of the perceptron (p). 1. Perceptron (P):
The perceptron model is also known as a single-layer neural network. This neural net contains only two layers: Input Layer Output Layer In this type of neural network, there are no hidden layers. It takes an input and calculates the weighted input for each node. Afterward, it uses an activation function (mostly a sigmoid function) for classification purposes. Applications: Classification. Encode Database (Multilayer Perceptron). Monitor Access Data (Multilayer Perceptron). 2. Feed Forward (FF):
A feed-forward neural network is an artificial neural network in which the nodes do not form a cycle. The perceptrons are arranged in layers where the input layer takes in input, and the output layer generates output. The hidden layers have no connection with the outer world; that’s why they are called hidden layers. Each layer is fully connected to the next. There are no back-loops. Backpropagation is generally used to update the weight values. Applications: Data Compression. Pattern Recognition. Computer Vision. Sonar Target Recognition. Speech Recognition. Handwritten Characters Recognition. 3. Radial Basis Network (RBN):
Radial basis function networks are generally used for function approximation problems. They use a
Radial Basis Function
as an
activation function . They are well-suited for continuous value outputs. RBIs determine how far the generated output is from the target output. RBNs behave as FF networks with different activation functions. Applications: Function Approximation. Timeseries Prediction. Classification. System Control. 4. Deep Feed-forward (DFF):
A deep feed-forward network uses more than one hidden layer. More hidden layers can reduce overfitting and improve generalization in some cases. Applications: Data Compression. Pattern Recognition. Computer Vision. ECG Noise Filtering. Financial Prediction. 5. Recurrent Neural Network (RNN):
RNNs process sequences by incorporating time-delayed inputs in hidden layers. They can remember information over time but may be slow to train and have limited memory for long-range dependencies. Applications: Machine Translation. Robot Control. Time Series Prediction. Speech Recognition. Speech Synthesis. Time Series Anomaly Detection. Rhythm Learning. Music Composition. 6. Long/Short Term Memory (LSTM):
LSTM networks introduce memory cells to handle long-range dependencies. They can remember data over longer sequences than standard RNNs. Applications: Speech Recognition. Writing Recognition. 7. Gated Recurrent Unit (GRU):
GRUs are a variation of LSTMs with three gates and no separate cell state. Applications: Polyphonic Music Modeling. Speech Signal Modeling. Natural Language Processing. 8. Auto Encoder (AE):
An autoencoder is an unsupervised model that learns to compress data and reconstruct it. Encoders convert input data to lower dimensions; decoders reconstruct the compressed data. Applications: Classification. Clustering. Feature Compression. 9. Variational Autoencoder (VAE):
A Variational Autoencoder uses a probabilistic approach to describe observations and their distribution. Applications: Interpolate Between Sentences. Automatic Image Generation. 10. Denoising Autoencoder (DAE):
DAEs aim to reduce noise in data and learn robust features so that the output is a cleaner version of the input. Applications: Feature Extraction. Dimensionality Reduction. 11. Sparse Autoencoder (SAE):
SAEs penalize activations to encourage sparsity, promoting localization of features in hidden representations. Applications: Feature Extraction. Handwritten digits Recognition. 12. Markov-related notes
A Markov chain is a system that transitions between states based on probabilistic rules; the next state depends only on the current state. Example states can be illustrated here. 13. Hopfield Network (HN)
Hopfield networks store patterns and memories and can recognize patterns from incomplete inputs. Applications: Optimization Problems. Image Detection And Recognition. Medical Image Recognition. Enhancing X-Ray Images. 14. Boltzmann Machine (BM)
A Boltzmann machine learns a probability distribution from data and can infer unseen data. It uses visible and hidden units with stochastic dynamics. Applications: Dimensionality Reduction. Classification. Regression. Collaborative Filtering. Feature Learning. 15. Restricted Boltzmann Machine (RBM)
RBMs are a variant of Boltzmann machines with no intra-layer connections, enabling efficient training. Applications: Filtering. Feature Learning. Classification. Risk Detection. Business and Economic analysis. 16. Deep Belief Network (DBN)
DBNs consist of stacked layers that learn features in an unsupervised manner before supervised training for classification. Applications: Retrieval of Documents/Images. Non-linear Dimensionality Reduction. 17-19. CNN family (brief)
Convolutional Neural Networks (CNNs) are primarily used for image classification, clustering, and object recognition. Deep networks enable hierarchical representations; other items include DNNs, Deconvolutional Networks (DN), Deep Convolutional Inverse Graphics Network (DC-IGN). Applications: Identify Faces, Street Signs, Tumors. Image Recognition. Video Analysis. NLP. Anomaly Detection. Drug Discovery. Time Series Forecasting. 18. Deconvolutional Networks (DN)
DNs are used to reconstruct signals from features; useful for image processing tasks such as super-resolution and related estimations. Applications: Image super-resolution. Surface depth estimation. Optical flow estimation. 19. Deep Convolutional Inverse Graphics Network (DC-IGN)
DC-IGN relates graphics representations to images, incorporating lighting, location, texture, and other attributes for image processing. Applications: Manipulation of human faces. 20. Generative Adversarial Network (GAN)
GANs learn to generate new data with statistics similar to training data, producing realistic-looking samples. Applications: Generate New Human Poses. Photos to Emojis. Face Aging. Super Resolution. Clothing Translation. Video Prediction. 21-23. Additional models
Liquid State Machine (LSM) and Extreme Learning Machine (ELM) are described with their typical use cases and attributes. Applications: Speech Recognition. Computer Vision. Figure references and notes about ESN, DRN, KN, SVM, NTM follow with brief descriptions and typical applications. 22-25. Extreme Learning Machine (ELM), Echo State Network (ESN), Deep Residual Network (DRN), Kohonen Network (KN)
Each model is described with core characteristics and applications such as classification, regression, clustering, and image analysis. 26-27. Support Vector Machines (SVM) and Neural Turing Machine (NTM)
26. SVMs are hybrid algorithms suitable for binary classifications, often not considered standard neural networks.
Applications:
faces, text, classification, bioinformatics, handwriting. 27. Neural Turing Machine (NTM): a controller interacting with an external memory, expanding the capabilities of standard neural networks for tasks like robotics and general AI. We hope you enjoyed this overview. If you have feedback or revisions, please email pub@towardsai.net. DISCLAIMER:
The views expressed are those of the author(s) and do not represent Carnegie Mellon University.
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Main Types of Neural Networks and its Applications — Tutorial. A tutorial on the main types of neural networks and their applications to real-world challenges. Nowadays, there are many types of neural networks in deep learning which are used for different purposes. This article goes through the most used topologies in neural networks, briefly explains how they work, and covers some of their real-world applications. Figure 1: Main types of neural networks.
Join us
| Towards AI Members | The Data-driven Community N owadays, there are many types of neural networks in deep learning which are used for different purposes. In this article, we will go through the most used topologies in neural networks, briefly introduce how they work, along some of their applications to real-world challenges. Figure 2: The perceptron: a probabilistic model for information storage and organization in the brain. This article is our third tutorial on neural networks; to start with our first one, check out neural networks from scratch with Python code and math in detail. Neural Network Topologies
Figure 3: Representation of the perceptron (p). 1. Perceptron (P):
The perceptron model is also known as a single-layer neural network. This neural net contains only two layers: Input Layer Output Layer In this type of neural network, there are no hidden layers. It takes an input and calculates the weighted input for each node. Afterward, it uses an activation function (mostly a sigmoid function) for classification purposes. Applications: Classification. Encode Database (Multilayer Perceptron). Monitor Access Data (Multilayer Perceptron). 2. Feed Forward (FF):
A feed-forward neural network is an artificial neural network in which the nodes do not form a cycle. The perceptrons are arranged in layers where the input layer takes in input, and the output layer generates output. The hidden layers have no connection with the outer world; that’s why they are called hidden layers. Each layer is fully connected to the next. There are no back-loops. Backpropagation is generally used to update the weight values. Applications: Data Compression. Pattern Recognition. Computer Vision. Sonar Target Recognition. Speech Recognition. Handwritten Characters Recognition. 3. Radial Basis Network (RBN):
Radial basis function networks are generally used for function approximation problems. They use a
Radial Basis Function
as an
activation function . They are well-suited for continuous value outputs. RBIs determine how far the generated output is from the target output. RBNs behave as FF networks with different activation functions. Applications: Function Approximation. Timeseries Prediction. Classification. System Control. 4. Deep Feed-forward (DFF):
A deep feed-forward network uses more than one hidden layer. More hidden layers can reduce overfitting and improve generalization in some cases. Applications: Data Compression. Pattern Recognition. Computer Vision. ECG Noise Filtering. Financial Prediction. 5. Recurrent Neural Network (RNN):
RNNs process sequences by incorporating time-delayed inputs in hidden layers. They can remember information over time but may be slow to train and have limited memory for long-range dependencies. Applications: Machine Translation. Robot Control. Time Series Prediction. Speech Recognition. Speech Synthesis. Time Series Anomaly Detection. Rhythm Learning. Music Composition. 6. Long/Short Term Memory (LSTM):
LSTM networks introduce memory cells to handle long-range dependencies. They can remember data over longer sequences than standard RNNs. Applications: Speech Recognition. Writing Recognition. 7. Gated Recurrent Unit (GRU):
GRUs are a variation of LSTMs with three gates and no separate cell state. Applications: Polyphonic Music Modeling. Speech Signal Modeling. Natural Language Processing. 8. Auto Encoder (AE):
An autoencoder is an unsupervised model that learns to compress data and reconstruct it. Encoders convert input data to lower dimensions; decoders reconstruct the compressed data. Applications: Classification. Clustering. Feature Compression. 9. Variational Autoencoder (VAE):
A Variational Autoencoder uses a probabilistic approach to describe observations and their distribution. Applications: Interpolate Between Sentences. Automatic Image Generation. 10. Denoising Autoencoder (DAE):
DAEs aim to reduce noise in data and learn robust features so that the output is a cleaner version of the input. Applications: Feature Extraction. Dimensionality Reduction. 11. Sparse Autoencoder (SAE):
SAEs penalize activations to encourage sparsity, promoting localization of features in hidden representations. Applications: Feature Extraction. Handwritten digits Recognition. 12. Markov-related notes
A Markov chain is a system that transitions between states based on probabilistic rules; the next state depends only on the current state. Example states can be illustrated here. 13. Hopfield Network (HN)
Hopfield networks store patterns and memories and can recognize patterns from incomplete inputs. Applications: Optimization Problems. Image Detection And Recognition. Medical Image Recognition. Enhancing X-Ray Images. 14. Boltzmann Machine (BM)
A Boltzmann machine learns a probability distribution from data and can infer unseen data. It uses visible and hidden units with stochastic dynamics. Applications: Dimensionality Reduction. Classification. Regression. Collaborative Filtering. Feature Learning. 15. Restricted Boltzmann Machine (RBM)
RBMs are a variant of Boltzmann machines with no intra-layer connections, enabling efficient training. Applications: Filtering. Feature Learning. Classification. Risk Detection. Business and Economic analysis. 16. Deep Belief Network (DBN)
DBNs consist of stacked layers that learn features in an unsupervised manner before supervised training for classification. Applications: Retrieval of Documents/Images. Non-linear Dimensionality Reduction. 17-19. CNN family (brief)
Convolutional Neural Networks (CNNs) are primarily used for image classification, clustering, and object recognition. Deep networks enable hierarchical representations; other items include DNNs, Deconvolutional Networks (DN), Deep Convolutional Inverse Graphics Network (DC-IGN). Applications: Identify Faces, Street Signs, Tumors. Image Recognition. Video Analysis. NLP. Anomaly Detection. Drug Discovery. Time Series Forecasting. 18. Deconvolutional Networks (DN)
DNs are used to reconstruct signals from features; useful for image processing tasks such as super-resolution and related estimations. Applications: Image super-resolution. Surface depth estimation. Optical flow estimation. 19. Deep Convolutional Inverse Graphics Network (DC-IGN)
DC-IGN relates graphics representations to images, incorporating lighting, location, texture, and other attributes for image processing. Applications: Manipulation of human faces. 20. Generative Adversarial Network (GAN)
GANs learn to generate new data with statistics similar to training data, producing realistic-looking samples. Applications: Generate New Human Poses. Photos to Emojis. Face Aging. Super Resolution. Clothing Translation. Video Prediction. 21-23. Additional models
Liquid State Machine (LSM) and Extreme Learning Machine (ELM) are described with their typical use cases and attributes. Applications: Speech Recognition. Computer Vision. Figure references and notes about ESN, DRN, KN, SVM, NTM follow with brief descriptions and typical applications. 22-25. Extreme Learning Machine (ELM), Echo State Network (ESN), Deep Residual Network (DRN), Kohonen Network (KN)
Each model is described with core characteristics and applications such as classification, regression, clustering, and image analysis. 26-27. Support Vector Machines (SVM) and Neural Turing Machine (NTM)
26. SVMs are hybrid algorithms suitable for binary classifications, often not considered standard neural networks.
Applications:
faces, text, classification, bioinformatics, handwriting. 27. Neural Turing Machine (NTM): a controller interacting with an external memory, expanding the capabilities of standard neural networks for tasks like robotics and general AI. We hope you enjoyed this overview. If you have feedback or revisions, please email pub@towardsai.net. DISCLAIMER:
The views expressed are those of the author(s) and do not represent Carnegie Mellon University.
#J-18808-Ljbffr