Deep learning is a subset of machine learning. It is powered by artificial neural networks (ANNs), which are algorithms modeled to simulate the human brain’s capability by learning from large amounts of data.
The “deep” in deep learning pertains to the depth of layers in a neural network. The more layers a network has between the input and output nodes, the more accurate its predictions will be. A deep learning network will have dozens or hundreds of layers; however, the deeper it is, the more parameters and computational resources it requires.
Deep learning neural networks consist of multiple layers with interconnected nodes. When data goes through a layer, its nodes process it and pass the results to the next layer. Each subsequent layer refines and optimizes its prediction based on the previous layer’s data. Data continues to flow forward—called forward propagation—until it reaches the output layer, with the neural network producing results.
A deep neural network can also run in reverse to correct errors. This is called backpropagation, and it utilizes algorithms to calculate errors. It adjusts the data’s weights and biases by moving backward through the layers, training the model better.
Deep learning takes in large datasets, automating most of them, and reduces the need for human intervention. However, to be accurate, a model must be trained with a lot of data.
Deep learning is the driving force behind numerous AI technologies used today. Some of its important applications include:
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