Deep learning is a set of algorithms used in Machine Learning. It is a part of machine learning methods based on artificial neural network. Learning can be supervised, unsupervised, or semi-supervised.
Deep learning architectures i.e. deep neural networks, recurrent neural networks and convolution neural networks have been applied to fields such asnatural language processing, computer vision, speech recognition, audio recognition, social network filtering, machine translation, drug design, bioinformatics, medical image analysis, material inspection and board game programs, where they have produced results in some cases superior to and comparable to human experts.
Deep learning used in
- Self-driving cars
- Deep learning in health care
- Voice search and voice-activated assistants
- Automatically adding sounds to silent movies
- Automatic machine translation
- Automatic text generation
- Automatic handwriting generation
- Image recognition
- Automatic image caption generation
- Automatic colorization
Neural Network and Deep Learning Neural Network
Artificial Neural Network or Neural Network was modeled after the human brain. Human has a mind to think and to perform the task in a particular situation, but how can a machine do that? For this purpose, an artificial brain was designed, which is known as a Neural Network. As the human brain has neurons for passing information, similarly neural network has nodes to perform that task. Nodes are the mathematical functions.
A Neural Network is based on the structure and functions of biological Neural Networks. A Neural Network itself changes or learn based on input and output. The information that flows through the network affect the structure of the artificial Neural Network because of its learning and changing property.
Deep Learning Neural Network is an advanced form of neural network. Unlike simple Neural Network, Deep Learning Neural Network have more than one hidden layer. Deep Learning Neural Network gets the more complex dataset as that your model is able to learn from. Deep Learning Neural Network is
Advantages of Neural Network
||Storing information in the entire network.
||In traditional programming, information is stored on the entire network, not on a database. If some piece of information is missed in one place, it does not prevent the network from functioning.
||Work with incomplete knowledge
||When our ANN is trained. The data may produce output either there is complete information or incomplete information. Here, the loss performance depends on the importance of the missing information.
||To train an ANN, it is necessary to determine the examples, and by showing these examples train it according to the desired output. The network can produce false output is the event cannot be shown to the network.
||Ability to make ML (Machine Learning)
||ANN has the capability to make a machine learn. ANN learn events and make decisions by commenting on similar events.
||Fault tolerance features
||If there is a corruption in one or more cell does not prevent it from generating output, and this feature makes it fault tolerance.
||ANN can perform more than one job at the same time because of its numeric strength quality.
Disadvantages of Neural Network
||Ann requires processors with parallel processing power according to their structure. The realization of equipment is dependent because of this reason.
||Network's unexplained behavior
||It is one of the most important problems of the ANN. It does not give any clue as to why and how, when it produces a probing solution.
||Determination of the proper network structure
||For determining the structure of the neural network, there are no specific rules available. With the help of experience, trial, and error, anappropriate network structure is achieved.
||Difficulty in showing the problem to the network
||ANN works with numerical information so that the problems are translated into numeric values before being introduced to ANN. For this reason, it is difficult to show the problem to the network.