How does an RBM work?
The working of an RBM can be divided into two main steps: training and inference.
Training an RBM
The training of an RBM involves adjusting the weights and biases to maximize the likelihood of the training data. This is done using a technique called Contrastive Divergence (CD). The CD algorithm compares the activations of the visible and hidden units in the RBM to update the weights and biases iteratively.
The training process starts by initializing the weights and biases randomly. Then, a training sample is presented to the RBM, and the activations of the hidden units are computed using the current weights and biases. Next, the activations of the hidden units are used to reconstruct the visible units, and the process is repeated for a few steps to obtain the reconstructed visible units. Finally, the updates to the weights and biases are computed based on the difference between the original visible units and the reconstructed visible units.
Inference with an RBM
Once the RBM is trained, it can be used for inference tasks such as generating new samples or performing classification. For generating new samples, the RBM starts with a random configuration of the visible units and then iteratively updates the hidden units and reconstructed visible units. This process allows the RBM to generate new samples that are similar to the training data.
For classification tasks, RBMs can be used as feature extractors. The hidden units can be viewed as a compressed representation of the input data, capturing the most relevant features. These features can then be fed into another classifier, such as a logistic regression model, to perform the actual classification task.
Restricted Boltzmann Machine (RBM) with Practical Implementation
In the world of machine learning, one algorithm that has gained significant attention is the Restricted Boltzmann Machine (RBM). RBMs are powerful generative models that have been widely used for various applications, such as dimensionality reduction, feature learning, and collaborative filtering. In this article, we will explore the concepts and steps involved in training and using RBMs, along with some good examples to solidify our understanding.