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ISBN: 9781617294433 1617294438 Year: 2017 Publisher: Shelter Island Manning Publications
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Artificial intelligence. Robotics. Simulation. Graphics --- Python (Computer program language) --- Machine Learning --- Neural networks (Computer science) --- Artificiële intelligentie --- Machine learning --- Machine learning. --- Neural networks (Computer science). --- Python (Computer program language). --- Machinaal leren --- Scripting languages (Computer science) --- Artificial neural networks --- Nets, Neural (Computer science) --- Networks, Neural (Computer science) --- Neural nets (Computer science) --- Artificial intelligence --- Natural computation --- Soft computing --- Learning, Machine --- Machine theory
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ISBN: 9780262035613 0262035618 0262337371 9780262337373 Year: 2016 Publisher: Massachusetts The MIT Press
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An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
Probability theory --- Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- Mathematical linguistics --- analyse (wiskunde) --- Machine learning. --- Artificiële intelligentie --- Machine learning --- Learning, Machine --- Artificial intelligence --- Machine theory --- למידה חשובית --- Apprentissage automatique --- Machine Learning --- Apprentissage automatique. --- Transfer Learning --- Learning, Transfer --- Machinaal leren --- 681.3*I2 --- 681.3*I2 Artificial intelligence. AI --- Artificial intelligence. AI --- deep learning --- machine learning --- artificiële intelligentie (AI) --- Informatique --- Intelligence artificielle
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ISBN: 9780691207544 0691207542 9780691207551 0691207550 9780691207995 Year: 2022 Publisher: Princeton Princeton University Press
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From social media posts and text messages to digital government documents and archives, researchers are bombarded with a deluge of text reflecting the social world. This textual data gives unprecedented insights into fundamental questions in the social sciences, humanities, and industry. Meanwhile new machine learning tools are rapidly transforming the way science and business are conducted. Text as Data shows how to combine new sources of data, machine learning tools, and social science research design to develop and evaluate new insights.Text as Data is organized around the core tasks in research projects using text?representation, discovery, measurement, prediction, and causal inference. The authors offer a sequential, iterative, and inductive approach to research design. Each research task is presented complete with real-world applications, example methods, and a distinct style of task-focused research.Bridging many divides?computer science and social science, the qualitative and the quantitative, and industry and academia?Text as Data is an ideal resource for anyone wanting to analyze large collections of text in an era when data is abundant and computation is cheap, but the enduring challenges of social science remain.Bron : https://politicalscience.stanford.edu/publications/text-data-new-framework-machine-learning-and-social-sciences
Text data mining --- Social sciences --- Machine learning --- Text mining --- Text analysis (Data mining) --- Text analytics --- Computational linguistics --- Data mining --- Learning, Machine --- Artificial intelligence --- Machine theory --- Data processing --- #SBIB:303H4 --- #SBIB:303H520 --- Informatica in de sociale wetenschappen --- Methoden sociale wetenschappen: techniek van de analyse, algemeen --- Methods in social research (general) --- Artificial intelligence. Robotics. Simulation. Graphics --- Social sciences - Data processing --- Grafische vormgeving --- Woordenboek --- Infografiek --- Text data mining. --- Machine learning. --- Data processing. --- Tekstanalyse --- Dataverwerking --- Sociaal-wetenschappelijk onderzoek
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