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Did you know how deep belief networks learn features through layers of hidden units?
In today's machine learning field, Deep Belief Network (DBN) is undoubtedly a revolutionary concept. As a generative graphical model, or a type of deep neural network, DBN consists of multiple layers of latent variables (called hidden units). There are connections between each layer, but the units in the same layer are not connected. This feature enables DBN to learn and reconstruct the probability distribution of its input data without supervision.
The learning process of DBN can be divided into two main steps. First, through a multi-layer structure, DBN serves as a feature detector for unsupervised learning; then, these layers can be further trained for supervised training to achieve classification purposes. It is worth noting that the core components of DBN are some simple unsupervised networks, such as Restricted Boltzmann Machines (RBM) or autoencoders. The hidden layer of each sub-network directly serves as the next layer. Visible layer.
"This layer-by-layer stacking structure allows DBN to be adjusted layer by layer with a fast unsupervised training process."
The training method of DBN is mainly carried out through RBM. This training method is called Contrastive Divergence (CD) proposed by Geoffrey Hinton. In order to approximate the ideal maximum likelihood method, CD learns and updates weights. When training a single RBM, gradient descent is used to update the weights, and the probability based on its visible vector is modeled according to the energy function.
"The weights are updated through the comparative divergence method, which has proven its effectiveness in practical applications."
During the training process, the initial visible unit is set as the training vector, and then the state of the hidden unit is updated based on the visible unit. After the hidden units are updated, the visible units are reconstructed based on the status of the hidden units. This process is called the "reconstruction step". Subsequently, based on the reconstructed visible units, the hidden units are updated again to complete a round of training.
When one RBM is trained, another RBM will be stacked on top of it, and the new visible layer will be taken from the training output of the previous layer. This cycle repeats until a preset stopping condition is met. Although the contrastive divergence method may not be an accurate approximation of maximum likelihood, it is quite effective in experiments.
Nowadays, DBN is widely used in many real-world applications and scenarios, including fields such as electroencephalogram analysis and drug discovery. Its deep learning characteristics enable DBN to capture the hierarchical structure in complex data and extract meaningful features.
"The emergence of this model has further promoted the development of deep learning technology and expanded its practical scope."
All in all, deep belief network, with its unique structure and training method, not only provides a powerful feature learning mechanism, but also paves the way for future artificial intelligence development. As technology continues to advance, how will this technology affect our lives and work?
