It is a subset of machine learning. It mimics the way our brain functions, i.e. it learns from experience. Algorithm of deep learning is used, especially when we have a massive number of input and output data.
Features of Deep learning
In deep learning, features extractions happen automatically; it means it is based on learning to focus on the right features by themselves, which requires very little guidance from the programmer.
Deep learning will learn the model, and it will understand which feature or variable is essential in predicting the outcome. When we have high dimensionality data, or we have extensive data, and it has a lot of features and a lot of predictor variables, we use deep learning. It will extract features by its own and understand which elements are essential in predicting the output. So this is the central concept behind deep learning.
How deep learning work
In Deep learning, the main aim was to re-engineer the human brain. It inspired by our brain structure. It studies the basic unit of brain called brain cell or neuron. Neurons are replicated in deep learning as artificial neurons.
Artificial neurons receive multiple inputs, and applies various transformations and functions and provide us with an output. Different information is input variables or predictor variables like our brain consists of numerous connected neurons called neural networks. Artificial neurons network called artificial neural networks. This is the basic concept behind deep learning.
So Deep learning is based on the idea of artificial neural networks which work like human brain.
Applications of Deep learning
Voice control Assistance
In voice control assistance, Siri is the best example which is used by Apple Company. We can tell Siri whatever we want it will search and display in front of us.
Automatic machine translation
In automatic machine translation, we can convert one language into another with the help of deep learning.
It learns through information only and form of biases issues.
Advantages of Deep learning
– It can quickly solve complex problems.
– Feature engineering can be executed automatically inside the deep learning model.
– It is flexible to an adapted new challenge in the future.
Disadvantage of deep learning
– It needs a broad amount of data to perform better.
– It is expensive.