Deep learning is a type of machine learning method which is based on artificial neural networks. In other terms, it is a part of the machine learning or artificial intelligence family that illustrates the way humans learn certain knowledge. This learning type is an important element of data science that consists of predictive modelling and statistics.
Deep learning is particularly beneficial for scientists who focus on collecting, analyzing and interpreting large amounts of data, making the process faster and easier. When compared with traditional machine learning algorithms that are linear, deep learning algorithms are put together in a hierarchy of rising abstraction and complexity. Owing to the advancement in Big Data Analytics, computers can now observe, learn, and react to complex situations in less time. This learning method has bolstered speech recognition, language translation, and image classification. Moreover, with the help of deep learning, one can solve any pattern recognition problem without human intervention.
How Deep Learning Works
Neural networks comprise layers of nodes, similar to a human brain with neurons. These nodes within separate layers are connected to adjacent layers. More the number of layers, the deeper the network.
In an artificial neural network, signals travel across nodes, and each node assigns corresponding weights. The heavier-weighted node is more effective and impacts its effect on the next layer of nodes. The final layer produces output through compiled weighted inputs as the process continues.