Titlebook: Artificial Intelligence; What Is Behind the T Gerhard Paa?,Dirk Hecker Book 2024 The Editor(s) (if applicable) and The Author(s), under exc

Diatribe

Some Basic Concepts of Machine Learning, the logistic regression model, which predicts the corresponding output for given inputs. The goal is to automatically find the relations between the existing input values and the output category in the data. For this purpose, a large number of numerical parameter values are modified step by step us

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Deep Learning Can Recognize Complex Relationships,represent arbitrary “curved” relationships between inputs and outputs. This chapter describes the properties of such deep neural networks and shows how to find the optimal parameters using the backpropagation method. It then discusses the problem of overfitting and how it can be solved using regular

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Image Recognition with Deep Neural Networks,ge objects and determining their position in the image. The majority of DNNs for image processing are Convolutional Neural Networks (CNN). They use layers with small receptive fields (convolutions), which are shifted over the pixel matrix of the input image. They are capable of detecting local image

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Understanding Spoken Language,or small time intervals as input. For speech processing, deep sequence-to-sequence models based on LSTM or transformers are used, which generate the recognized text. Alternatively, Convolutional Neural Networks are employed. A hybrid model of Sequence-to-Sequence and CNN models is able to achieve a

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Learning Optimal Policies,ieving the highest possible sum of rewards over time. An action is determined based on the current state and affects the reward, often many time steps later. Examples applications include games, robotic controls, and self-driving cars. Deep neural networks (DNNs) can be used to assign a sum of expec

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Creative Artificial Intelligence and Emotions, adversarial networks (GANs) are able to create images with specific properties or style features. In addition, they can convert images from one type to another, such as a photo to a painting. For authoring texts, there are language models that can invent new complex stories and formulate them in fl

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Understanding Spoken Language,iants of spatio-temporal convolutional layers. More difficult is the description of videos by subtitles, which can be done for example with the help of transformer translation models. In a last section, the influence of noise on speech recognition and the potential danger of adversarial attacks are