Deep learning is a subfield of machine learning that mimics the workings of the human brain in processing data for use in decision making. It is also known as deep neural learning or deep neural network. Deep learning algorithms are designed based on artificial neural networks, particularly, neural networks with three or more layers. These layers work to build an improved model of predictions and behaviors through a system of “neurons”, which is inspired by the human brain structure and functionality.
How Deep Learning works
Deep learning models are built using neural networks that have a significant number of layers. Each layer of neurons takes in some kind of input, performs a transformation or computation on that input, and then passes the output onto the next layer. The first layer will typically extract low-level features (like lines or edges in an image), and each subsequent layer uses the output of the previous layer to extract higher-level features (like shapes or objects).
In the training phase, the model makes predictions based on the input data and internal parameters, it then compares these predictions with the actual target outcomes. The differences between the predicted and actual outcomes, called the error, are then backpropagated (passed backward through the network), and the model adjusts its internal parameters to minimize this error. Through the process of this iterative optimization, the model learns the optimal parameters that can make accurate predictions.
These deep learning models can handle large volumes of unstructured and high-dimensional data, making them extremely useful for many AI tasks, like computer vision and speech recognition, natural language processing, and autonomous driving.
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