An autoencoder is a type of deep learning network that is trained to replicate its input data. Autoencoders have surpassed traditional engineering techniques in accuracy and performance on many applications, including anomaly detection, text generation, image generation, image denoising, and digital communications.

Train a sparse autoencoder with default settings. Reconstruct the abalone shell ring data using the trained autoencoder. Compute the mean squared reconstruction error. Load the sample data.

To generate text, you can use the decoder to reconstruct text from arbitrary input. This example trains an autoencoder to generate text. The encoder uses a word embedding and an LSTM operation to map the input text into latent vectors. The decoder uses an LSTM operation and the same embedding to reconstruct the text from the latent vectors.

Interactively build and edit deep learning networks in Deep Network Designer. Generate MATLAB ® code to recreate designing a network in Deep Network Designer. This example shows how to import a pretrained TensorFlow™ network and view …

If you have unlabeled data, perform unsupervised learning with autoencoder neural networks for feature extraction.

Nov 8, 2024 · I'm trying out MATLAB's deep network designer and having trouble setting up a simple autoencoder for MNIST images. Can anyone provide an example of how to read in MNIST images and feed them into a simple autoencoder so …

This example shows how to train stacked autoencoders to classify images of digits. Neural networks with multiple hidden layers can be useful for solving classification problems with complex data, such as images. Each layer can learn features at a different level of abstraction.

Build and edit deep learning networks interactively using the Deep Network Designer app. Using this app, you can: Import and edit networks. Build new networks from scratch. Add new layers and create new connections. View and edit layer properties. Combine networks. Import custom layers. Generate MATLAB ® code to create the network architecture.

This example shows how to create a simple recurrent neural network for deep learning sequence classification using Deep Network Designer. To train a deep neural network to classify sequence data, you can use an LSTM network.

Generic Deep Autoencoder for Time-Series. This toolbox enables the simple implementation of different deep autoencoder. The primary focus is on multi-channel time-series analysis. Each autoencoder consists of two, possibly deep, neural networks - the encoder and the decoder.