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Machine learning. --- Machine Learning. --- Transfer Learning --- Learning, Machine --- Learning, Transfer --- Artificial intelligence --- Machine theory --- Machine learning --- Machine Learning

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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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In this extended meditation, Jean Lave interweaves analysis of the process of apprenticeship among the Vai and Gola tailors of Liberia with reflections on the evolution of her research on those tailors in the late 1970s. In so doing, she provides both a detailed account of her apprenticeship in the art of sustained fieldwork and an insightful overview of thirty years of changes in the empirical and theoretical facets of ethnographic practice. Examining the issues she confronted in her own work, Lave shows how the critical questions raised by ethnographic research erode conventional assumptions, altering the direction of the work that follows. As ethnography takes on increasing significance to an ever widening field of thinkers on topics from education to ecology, this erudite but accessible book will be essential to anyone tackling the question of what it means to undertake critical and conceptually challenging fieldwork. Apprenticeship in Critical Ethnographic Practice explains how to seriously explore what it means to be human in a complex world-and why it is so important.

Vai (African people) --- Gola (African people) --- Tailors --- Ethnology --- Social life and customs. --- Apprentices. --- apprenticeship, vai, gola, tailors, liberia, fieldwork, anthropology, sociology, labor, africa, ethnology, nonfiction, academia, institutional arrangements, training, methodology, research, scholarship, learning transfer, development, education, skill building, monrovia, happy corner, artisan, activity, praxis, theory, communities of practice.

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Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques: A MATLAB Based Approach presents how machine learning and biomedical signal processing methods can be used in biomedical signal analysis. Different machine learning applications in biomedical signal analysis, including those for electrocardiogram, electroencephalogram and electromyogram are described in a practical and comprehensive way, helping readers with limited knowledge. Sections cover biomedical signals and machine learning techniques, biomedical signals, such as electroencephalogram (EEG), electromyogram (EMG) and electrocardiogram (ECG), different signal-processing techniques, signal de-noising, feature extraction and dimension reduction techniques, such as PCA, ICA, KPCA, MSPCA, entropy measures, and other statistical measures, and more. This book is a valuable source for bioinformaticians, medical doctors and other members of the biomedical field who need a cogent resource on the most recent and promising machine learning techniques for biomedical signals analysis.--

Biosensors. --- Artificial intelligence --- Signal processing --- Medical applications. --- Digital techniques. --- Signal Processing, Computer-Assisted. --- Machine Learning. --- Spectrum Analysis. --- Analysis, Spectrum --- Spectrometry --- Spectroscopy --- Transfer Learning --- Learning, Machine --- Learning, Transfer --- Digital Signal Processing --- Signal Interpretation, Computer-Assisted --- Signal Processing, Digital --- Computer-Assisted Signal Interpretation --- Computer-Assisted Signal Interpretations --- Computer-Assisted Signal Processing --- Interpretation, Computer-Assisted Signal --- Interpretations, Computer-Assisted Signal --- Signal Interpretation, Computer Assisted --- Signal Interpretations, Computer-Assisted --- Signal Processing, Computer Assisted --- Fetal Monitoring --- Monitoring, Physiologic --- Data Compression --- Digital signal processing --- Digital communications --- Digital electronics --- Medicine --- Biodetectors --- Biological detectors --- Biological sensors --- Biomedical detectors --- Biomedical sensors --- Detectors --- Medical instruments and apparatus --- Physiological apparatus --- Data processing

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Nature Machine Intelligence will publish high-quality original research and reviews in a wide range of topics in machine learning, robotics and AI. The journal will also explore and discuss the significant impact that these fields are beginning to have on other scientific disciplines as well as many aspects of society and industry. There are countless opportunities where machine intelligence can augment human capabilities and knowledge in fields such as scientific discovery, healthcare, medical diagnostics and safe and sustainable cities, transport and agriculture. At the same time, many important questions on ethical, social and legal issues arise, especially given the fast pace of developments Nature Machine Intelligence will provide a platform to discuss these wide implications — encouraging a cross-disciplinary dialogue — with Comments, News Features, News & Views articles and also Correspondence.

Engineering --- Engineering, general --- Construction --- Industrial arts --- Technology --- Artificial intelligence. --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Artificial Intelligence. --- Machine Learning. --- Robotics. --- Soft Robotics --- Remote Operations (Robotics) --- Telerobotics --- Operation, Remote (Robotics) --- Operations, Remote (Robotics) --- Remote Operation (Robotics) --- Robotic, Soft --- Robotics, Soft --- Soft Robotic --- Exoskeleton Device --- Transfer Learning --- Learning, Machine --- Learning, Transfer --- Computational Intelligence --- AI (Artificial Intelligence) --- Computer Reasoning --- Computer Vision Systems --- Knowledge Acquisition (Computer) --- Knowledge Representation (Computer) --- Machine Intelligence --- Acquisition, Knowledge (Computer) --- Computer Vision System --- Intelligence, Computational --- Intelligence, Machine --- Knowledge Representations (Computer) --- Reasoning, Computer --- Representation, Knowledge (Computer) --- System, Computer Vision --- Systems, Computer Vision --- Vision System, Computer --- Vision Systems, Computer --- Heuristics --- Companion Robots --- Social Robots --- Socially Assistive Robots --- Assistive Robot, Socially --- Assistive Robots, Socially --- Companion Robot --- Robot, Companion --- Robot, Social --- Robot, Socially Assistive --- Robots, Companion --- Robots, Social --- Robots, Socially Assistive --- Social Robot --- Socially Assistive Robot