Procesul de deeplearning al aplicațiilor cu inteligență artificială
The deeplearning process of artificial intelligence applications

Author(s): Sorin Lică, Ciprian Eugeniu Constantin
Subject(s): Politics / Political Sciences, Social Sciences, Law, Constitution, Jurisprudence, Library and Information Science, Criminal Law, Public Law, Security and defense, Politics and law, EU-Legislation, Peace and Conflict Studies, Labour and Social Security Law
Published by: Editura Universitară
Keywords: deeplearning; deepfake; deepnude; apps; artificial intelligence; browser;
Summary/Abstract: Deeplearning is a branch of machine learning and artificial intelligence (AI) that focuses on training deep neural networks to learn and make decisions or make predictions. This technology is inspired by the structure and function of the human brain.Deeplearning models are made up of several layers of interconnected artificial neurons, called artificial neural networks. Each layer takes the output from the previous layer, processes it and sends it to the next layer. Layers close to the input are responsible for extracting lower-level features, while deeper layers learn higher-level representations or abstractions.One of the main advantages of deeplearning is its ability to automatically learn relevant features or representations from raw data, eliminating the need for manual engineering. This allows deeplearning models to excel in tasks such as image and speech recognition, natural language processing, recommender systems, and more.Training deeplearning models typically involves providing a large set of labeled data and using an algorithm called backpropagation to adjust connection weights between neurons, minimizing the difference between predicted and actual outputs. This process is computationally intensive and often requires significant computational resources such as specialized hardware (e.g., GPUs) or distributed computing systems.

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