Table of Contents
ToggleWhat are neural networks?
Let’s get deep into the universe of types of neural networks.
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Feedforward neural networks (FNNs):
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Artificial Neural Network (ANN):
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Recurrent Neural Networks (RNNs):
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Long Short-Term Memory Network (LSTM):
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Modular Neural Network:
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Autoencoders:
Neural networks are designed for unsupervised learning and dimensionality reduction. They are made up of an encoder network, which compresses input data into a lower dimensional representation, and a decoder network, which reconstructs the original input using the compressed form. Autoencoders are commonly used for applications including anomaly detection, data denoising, and feature learning.
7.Generative adversarial networks:
Conclusion:
FAQs
A feedforward neural network is a type of neural network in which information travels from input nodes via hidden layers to output nodes. The network architecture includes no cycles or loops.
CNNs are specially built to handle structured grid data, such as photos. They extract characteristics using convolutional layers and are very successful in applications like image recognition and computer vision.
LSTMs employ memory cells with gating methods to selectively retain or forget information over time, allowing them to capture long-term dependencies in sequential data while avoiding the vanishing gradient issue.
GRUs have a simpler design and fewer parameters, making them more computationally efficient while still capturing temporal connections in sequential data.