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What is Deep Learning? Use Cases, Examples, Benefits in 2022
Deep learning, also called deep structured learning or hierarchical learning, is a set of machine learning methods which is part of the broader family of artificial neural network based machine learning methods. Like other machine learning methods, deep learning allows businesses to predict outcomes. A simple example is to predict which customers are likely to buy if they receive discounted offers. Improved models allow businesses to save costs and increase sales.
Deep learning is a subset of machine learning that is inspired by the structure and function of the human brain. It involves the use of neural networks, which are composed of layers of interconnected nodes that can learn to recognize complex patterns in data.
Neural networks are modeled after the neurons in the human brain and are designed to mimic the way that the brain processes information. Each node in a neural network receives input from the nodes in the previous layer and produces an output, which is passed on to the nodes in the next layer.
The architecture of a neural network can vary depending on the problem being solved, but some common types of neural networks include:
Feedforward neural networks: This is the simplest type of neural network, where the input data is fed into the input layer, and the output is produced in the output layer. There can be one or more hidden layers in between.
Convolutional neural networks (CNNs): CNNs are commonly used for image and video recognition tasks. They use filters to extract features from the input data, and then these features are passed through multiple layers of neural networks for classification.
Recurrent neural networks (RNNs): RNNs are commonly used for sequential data, such as text and speech recognition. They use a feedback loop that allows information to be passed from one step of the sequence to the next.
Deep learning algorithms, such as neural networks, are able to automatically learn from large amounts of data and can achieve state-of-the-art performance on a wide range of tasks, such as image and speech recognition, natural language processing, and autonomous vehicles. However, they require a large amount of labeled data and significant computational resources for training, which can be a limitation in some contexts.