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Using multi-layer artificial nerve networks, the machine learning that learns complex nucleus is lower.
Deep learning is the bottom of a machine learning, using artificial nerve networks composed of numerous neuron layers connected to each other, using raw data (image, sound, text) automatic and hierarchical attribute extract. The adjective "Derin" comes from the presence of a large number of hidden layers between input and output; each layer learns more abstract representations than the previous one. For example, the first layers in a image recognition network represent edge and color transitions, medium layers texture and shapes, the final layers represent object categories. The ability to learn this auto attribute has eliminated dependence on attributes designed by experts in traditional machine learning.
It has been possible to combine three factors that make deep learning practically burst: providing huge amount of label data on the internet, maturation of educational algorithms such as the parallel calculation power of the GPUs to the matrix palpits, and back-liferation. As a result, in areas such as image recognition, natural language processing, speech recognition and game play, systems that come to human performance or exceed him were revealed. Today’s large language models, diffusion-based visual producers and speech synthesizers are always applied with different architectures of deep learning; the area is the most decisive technical foundation of modern artificial intelligence.
It has been possible to combine three factors that make deep learning practically burst: providing huge amount of label data on the internet, maturation of educational algorithms such as the parallel calculation power of the GPUs to the matrix palpits, and back-liferation. As a result, in areas such as image recognition, natural language processing, speech recognition and game play, systems that come to human performance or exceed him were revealed. Today’s large language models, diffusion-based visual producers and speech synthesizers are always applied with different architectures of deep learning; the area is the most decisive technical foundation of modern artificial intelligence.