Ever wondered how DeepMind’s AlphaGo easily defeated Lee Sedol, one of the best Go players.
No one saw it coming. It totally seemed impossible, but with the help of deep learning, anything is possible today.
Being a subset of machine learning, it now lies at the heart of multiple innovations seen across industries. From self-driving cars to image processing and natural language processing — it’s already here. Most often people think that artificial neural networks and deep learning are terms often used interchangeably, which is incorrect. Not all neural networks can be called “deep” with multiple hidden layers and not all deep learning architectures can be called neural networks.
However, we will further talk more briefly about neural networks and how they can be used to solve multiple complex problems. Although you will find many neural networks present out there, we will only be talking about the ones that are commonly used in the current industries.
Let’s look at some of the important neural network models in deep learning:
- Deep Belief Network
Deep Belief Network (DBN), with the help of unsupervised machine learning and probabilities, helps generate output. The DBN is different from other models since each layer is orderly regulated and learns the complete input. The DBN encompasses undirected layers, directed layers, and binary latent variables.
In the DBS network, each of the hidden sub-network layers is visible to the next layer. Therefore, enabling a fast layer-by-layer unsupervised training model making contrastive divergence applicable to each of the sub-network. This gets started with the lowest layer that is visible.
Algorithms that are known as greedy learning algorithms are used to train the DBN. These algorithms incorporate the learning one layer at a time. As a result, a different version of data gets added to each layer. Therefore, every layer will use the output from the previous layer to be placed at its input.