Deep Belief Networks and Autoencoders

Deep Belief Networks (DBN) and Autoencoders, Let’s take a look at DBNs and how they are created on top of RBMs.

If you haven’t read the previous posts yet, you can read them by clicking the below links.

1. Introduction to Machine Learning with TensorFlow »
2. Introduction to Deep Learning »
3. Convolutional Neural Networks »
4. Introduction to Recurrent Neural Networks (RNN) »
5. Restricted Boltzmann Machine (RBM) »

Deep Belief Network (DBN)

A DBN is a network that was created to overcome a problem that existed in standard artificial neural networks.

Backpropagation is a phenomenon that might result in “local minima” or “vanishing gradients.”

DBN is designed to solve this problem by stacking numerous RBMs.

Applications

1. Classification (Eg: image classification etc.)

DBN does not require a large set of labeled data to train. A small set is sufficient because feature extraction is performed unsupervised by a stack of RBMs.

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Autoencoders

Let’s take a look at autoencoders. Autoencoders, like RBMs, was created to address the problem of extracting useful features.

And, once again, autoencoders, like RBMs, attempt to produce a given input, but with a slightly different network design and learning mechanism.

Autoencoders take a series of unlabeled inputs and encode them into shortcodes, which they then utilize to recreate the original image while collecting the most important information from the data.

In unsupervised machine learning, autoencoders are a form of network. Assume you wish to extract a person’s feeling or emotion from a photograph

Take a look at an image of 256 by 256 pixels. This indicates that over 65000 pixels are involved in defining the input’s dimension.

As a result, the goal is to extract the most relevant elements from an image and represent each image with those features that have lower dimensions. This type of problem is ideally suited to an autoencoder.

It is a form of network that analyses all of the images in your data set and automatically extracts some helpful properties so that it can discriminate between images using those features.

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Autoencoder is used for tasks that involve

1. Feature extraction
2. Data Compression
3. Data generative models for learning
4. Dimensionality reduction.

For these reasons, the autoencoder was a game-changer in the field of unsupervised learning research.

You now have a good understanding of what Deep Belief networks and Autoencoders are and how they work.

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