This repository contains implementations and examples of different types of autoencoders for various applications.

• Purpose: Vanilla autoencoders learn efficient representations of data by compressing it into a latent-space representation and then reconstructing the original input from this representation.

• Architecture: Typically consists of an encoder network that compresses the input data and a decoder network that reconstructs the input from the latent space representation.

• Usage: Include instructions on how to train the vanilla autoencoder, example datasets used, and how to evaluate its performance.

• Purpose: Denoising autoencoders are trained to remove noise from corrupted input data, thereby learning more robust feature representations.

• Architecture: Similar to vanilla autoencoders but trained with noisy input and optimized to reconstruct clean data.

• Usage: Describe how to prepare and use noisy datasets for training, and provide examples of denoising results.

• Purpose: Convolutional autoencoders use convolutional layers for both the encoder and decoder, making them suitable for image data where spatial relationships are important.

• Architecture: Encoder and decoder networks consist of convolutional and pooling layers, allowing them to learn hierarchical features.

• Usage: Specify image preprocessing steps, training details, and demonstrate reconstruction quality with image examples.

• Purpose: Variational autoencoders learn a latent-space representation that follows a probabilistic distribution, enabling generation of new data points.

• Architecture: Includes an encoder that learns the parameters of the latent distribution and a decoder that generates data points from sampled latent variables.

• Usage: Discuss the use of latent variables for generating new data, training VAEs with variational lower bounds, and provide examples of generated outputs.

Include a section demonstrating how to use each type of autoencoder in practice, with code snippets and example outputs if possible.

Specify the license under which this repository is shared (e.g., MIT License).

Guidelines for contributions, if applicable, such as how to submit issues or pull requests.