How Deep Learning is different from Neural Network
With advancements in technology, we have discovered newer ways and methods that help us in solving our problems. Although technology and development involving technology have helped in making our lives easier, with the introduction of newer terms, the confusion in understanding their literal meaning and differentiating between them has become a challenging task for us. The same is the scenario with the terms: Deep learning and Neural network. They are often misinterpreted and used falsely.
Deep learning is a subset in machine learning that gives the system the capability to function like a human brain and imitate patterns that our brain does for making decisions. A deep learning system learns from observing different kinds and patterns of data and drawing conclusions based on them. Deep learning is a deep neural network that is made up of many different layers, and each layer comprises many different nodes.
Neural networks are based on algorithms that are present in our brain and help in its functioning. A Neural network interprets Numerical patterns which may be present in the form of Vectors. These vectors are translated with the help of neural networks. The principal work that a neural network performs is the classification and grouping of data based on similarities. The most important advantage about a neural network is that it can easily adapt itself to the changing pattern of output, and you needn’t modify it every time based on the input that you provide.
Main Differences Between Deep Learning and Neural Network
- Deep learning is a complex form of neural network. There are many different layers in a deep learning network which makes it way more complex whereas a Neural network consists of one input one output and hardly one hidden layer
- A deep learning system provides high efficiency and performance for the completion of tasks, while a neural network performs tasks with low efficiency when compared to a deep learning system.
- The major components in a deep learning unit are Large PSU, GPU, and a Huge RAM, while that of a neural network are Neurons, learning rate, Connections, Propagation functions, and weight.
- Deep learning networks being complex, requires a lot of time to train the network, while a neural network requires comparatively very little time to train the network.