Artificial neural networks, which are used in deep learning algorithms, are modeled after the structure and operation of the brain. While developing a deep learning network, Alexey Grigorevich Ivakhnenko published the first general in the middle of the 1960s. A variety of domains, including computer vision, speech recognition, natural language processing, etc., are well suited for deep learning.

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• Supervised learning is a system in which both input and desired output data are provided. Input and output data are labeled to provide a learning basis for future data processing.
• Unsupervised procedure does not need labeling information explicitly, and the operations can be carried out without the same. The common unsupervised learning method is cluster analysis. It is used for exploratory data analysis to find hidden patterns or grouping in data.

• Computer vision
• Natural language processing and pattern recognition
• Image recognition and processing
• Machine translation
• Sentiment analysis
• Question Answering system
• Object Classification and Detection
• Automatic Handwriting Generation
• Automatic Text Generation.

Both shallow and deep networks can approximate any function and are equally effective. Deeper networks, however, can be substantially more productive in terms of computation and number of parameters for the same degree of accuracy. Deep representations can be produced by deeper networks. The network picks up a new, more abstract representation of the input at each tier.

The most frequent problem in deep learning is overfitting. Typically, it happens when a deep learning algorithm recognises the sound of particular data. It also manifests when a specific algorithm is well suited to the data and manifests when a model or algorithm exhibits high variance and low bias.