Autoencoder
A neural network that learn how to compress and decompress data. They are trained to reconstruct the input data from a compressed representation, which is further learned by the network during training of the model. It can be used for tasks such as image compression, dimensionality reduction, and denoising. Autoencoders are feedforward neural networks with an encoder and a decoder. They aim to map input data to a lower-dimensional representation and then reconstruct the data.
When input data is compressed into a lower-dimensional representation called the latent space, the first part of the neural network to do this is called an encoder. In order to facilitate effective data reconstruction by the decoder, it extracts important aspects from the data. Reducing dimensionality, learning features, and denoising data are common applications for autoencoders. The encoder maps the input data x to an encoding or hidden representation ℎ using a set of weights (We) and biases (be ):
where fe is the activation function of the encoder
The neural network component that reverses the encoding process is called the decoder. By using the encoder’s compact, lower-dimensional representation (latent space), it reconstructs the original input data. Autoencoders can learn to denoise data or create new data based on learnt characteristics by using the decoder’s goal of closely replicating the input. The decoder maps the encoding ℎ back to the original input x using a different set of weights (Wd) and biases (bd ):
An autoencoder’s loss function measures how much the input data differs from its reconstruction. It calculates the discrepancy between the original and decoder-generated data. In order to help the model acquire useful feature representations in the latent space and provide correct reconstructions, the objective is to minimize this loss. The loss function measures the difference between the input x and the reconstructed output x’:
Unsupervised Neural Network Models
Unsupervised learning is an intriguing area of machine learning that reveals hidden structures and patterns in data without requiring labelled samples. Because it investigates the underlying relationships in data, it’s an effective tool for tasks like anomaly identification, dimensionality reduction, and clustering. There are several uses for unsupervised learning in domains like computer vision, natural language processing, and data analysis. Through self-sufficient data interpretation, it provides insightful information that enhances decision-making and facilitates comprehension of intricate data patterns.
There are many types of unsupervised learning, but here in this article, we will be focusing on Unsupervised neural network models.
Table of Content
- Unsupervised Neural Network
- Autoencoder
- Restricted Boltzmann Machine
- Self-Organizing maps (SOM)
- Generative Adversarial Networks (GANs)
- Implementation of Restricted Boltzmann Machine
- Advantages of Unsupervised Neural network models
- Disadvantages of Unsupervised Neural network models
Contact Us