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The twenty last years have been marked by an increase in available data and computing power. In parallel to this trend, the focus of neural network research and the practice of training neural networks has undergone a number of important changes, for example, use of deep learning machines. The second edition of the book augments the first edition with more tricks, which have resulted from 14 years of theory and experimentation by some of the world's most prominent neural network researchers. These tricks can make a substantial difference (in terms of speed, ease of implementation, and accuracy) when it comes to putting algorithms to work on real problems.

Neural networks (Computer science) --- Engineering & Applied Sciences --- Computer Science --- Artificial neural networks --- Nets, Neural (Computer science) --- Networks, Neural (Computer science) --- Neural nets (Computer science) --- Computer science. --- Computers. --- Algorithms. --- Artificial intelligence. --- Pattern recognition. --- Complexity, Computational. --- Computer Science. --- Computation by Abstract Devices. --- Artificial Intelligence (incl. Robotics). --- Algorithm Analysis and Problem Complexity. --- Pattern Recognition. --- Complexity. --- Information Systems Applications (incl. Internet). --- Complexity, Computational --- Electronic data processing --- Machine theory --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Logic machines --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Algorism --- Algebra --- Arithmetic --- Automatic computers --- Automatic data processors --- Computer hardware --- Computing machines (Computers) --- Electronic calculating-machines --- Electronic computers --- Hardware, Computer --- Computer systems --- Cybernetics --- Calculators --- Cyberspace --- Informatics --- Science --- Foundations --- Computer software. --- Optical pattern recognition. --- Engineering. --- Artificial Intelligence. --- Construction --- Industrial arts --- Technology --- Optical data processing --- Pattern perception --- Perceptrons --- Visual discrimination --- Software, Computer --- Artificial intelligence --- Natural computation --- Soft computing --- Computational complexity. --- Application software. --- Application computer programs --- Application computer software --- Applications software --- Apps (Computer software) --- Computer software --- Pattern recognition systems. --- Dynamics. --- Nonlinear theories. --- Theory of Computation. --- Automated Pattern Recognition. --- Applied Dynamical Systems. --- Computer and Information Systems Applications. --- Nonlinear problems --- Nonlinearity (Mathematics) --- Calculus --- Mathematical analysis --- Mathematical physics --- Dynamical systems --- Kinetics --- Mathematics --- Mechanics, Analytic --- Force and energy --- Mechanics --- Physics --- Statics --- Pattern classification systems --- Pattern recognition computers --- Computer vision

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Brain and Cognitive Engineering is a converging study field to derive a better understanding of cognitive information processing in the human brain, to develop “human-like” and neuromorphic artificial intelligent systems and to help predict and analyze brain-related diseases. The key concept of Brain and Cognitive Engineering is to understand the Brain, to interface the Brain, and to engineer the Brain. It could help us to understand the structure and the key principles of high-order information processing on how the brain works, to develop interface technologies between a brain and external devices and to develop artificial systems that  can ultimately mimic human brain functions. The convergence of behavioral, neuroscience and engineering research could lead us to advance health informatics and personal learning, to enhance virtual reality and healthcare systems, and to “reverse engineer” some brain functions and build cognitive robots. In this book, four different recent research directions are presented: Non-invasive Brain-Computer Interfaces, Cognitive- and Neural-rehabilitation Engineering, Big Data Neurocomputing, Early Diagnosis and Prediction of Neural Diseases. We cover numerous topics ranging from smart vehicles and online EEG analysis, neuroimaging for Brain-Computer Interfaces, memory implantation and rehabilitation, big data computing in cultural aspects and cybernetics to brain disorder detection. Hopefully this will provide a valuable reference for researchers in medicine, biomedical engineering, in industry and academia for their further investigations and be inspiring to those who seek the foundations to improve techniques and understanding of the Brain and Cognitive Engineering research field.

Neurology --- Medicine --- Health & Biological Sciences --- Brain --- User-centered system design. --- Human-computer interaction. --- Neural computers. --- Diseases. --- Neural net computers --- Neural network computers --- Neurocomputers --- Computer-human interaction --- Human factors in computing systems --- Interaction, Human-computer --- Cognitive engineering (System design) --- Participatory design (System design) --- UCD (System design) --- Usability engineering (System design) --- User-centered design (System design) --- Brain diseases --- Medicine. --- Neurosciences. --- Bioinformatics. --- Neurobiology. --- Biomedicine. --- Computational Biology/Bioinformatics. --- Electronic digital computers --- Natural computation --- Artificial intelligence --- Human engineering --- User-centered system design --- User interfaces (Computer systems) --- System design --- Human-computer interaction --- Psychology, Pathological --- Neurosciences --- Bio-informatics --- Biological informatics --- Biology --- Information science --- Computational biology --- Systems biology --- Neural sciences --- Neurological sciences --- Neuroscience --- Medical sciences --- Nervous system --- Data processing

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The development of “intelligent” systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to “intelligent” machines. Forsensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper, non-robust and unsafe decisions and actions. Before deploying an AI system, we see a strong need to validate its behavior, and thus establish guarantees that it will continue to perform as expected when deployed in a real-world environment. In pursuit of that objective, ways for humans to verify the agreement between the AI decision structure and their own ground-truth knowledge have been explored. Explainable AI (XAI) has developed as a subfield of AI, focused on exposing complex AI models to humans in a systematic and interpretable manner. The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of interpretable and explainable AI and AI techniques that have been proposed recently reflecting the current discourse in this field and providing directions of future development. The book is organized in six parts: towards AI transparency; methods for interpreting AI systems; explaining the decisions of AI systems; evaluating interpretability and explanations; applications of explainable AI; and software for explainable AI.

Artificial intelligence. --- Computer vision. --- Computer science. --- Computer security. --- Computer network architectures. --- Artificial Intelligence. --- Image Processing and Computer Vision. --- Computing Milieux. --- Systems and Data Security. --- Computer Systems Organization and Communication Networks. --- Architectures, Computer network --- Network architectures, Computer --- Computer architecture --- Computer privacy --- Computer system security --- Computer systems --- Computers --- Cyber security --- Cybersecurity --- Electronic digital computers --- Protection of computer systems --- Security of computer systems --- Data protection --- Security systems --- Hacking --- Informatics --- Science --- Machine vision --- Vision, Computer --- Artificial intelligence --- Image processing --- Pattern recognition systems --- 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 --- Protection --- Security measures --- Optical data processing. --- Computers. --- Computer organization. --- Organization, Computer --- Automatic computers --- Automatic data processors --- Computer hardware --- Computing machines (Computers) --- Electronic calculating-machines --- Electronic computers --- Hardware, Computer --- Cybernetics --- Calculators --- Cyberspace --- Optical computing --- Visual data processing --- Integrated optics --- Photonics --- Optical equipment --- Neural networks (Computer science)

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Designing molecules and materials with desired properties is an important prerequisite for advancing technology in our modern societies. This requires both the ability to calculate accurate microscopic properties, such as energies, forces and electrostatic multipoles of specific configurations, as well as efficient sampling of potential energy surfaces to obtain corresponding macroscopic properties. Tools that can provide this are accurate first-principles calculations rooted in quantum mechanics, and statistical mechanics, respectively. Unfortunately, they come at a high computational cost that prohibits calculations for large systems and long time-scales, thus presenting a severe bottleneck both for searching the vast chemical compound space and the stupendously many dynamical configurations that a molecule can assume. To overcome this challenge, recently there have been increased efforts to accelerate quantum simulations with machine learning (ML). This emerging interdisciplinary community encompasses chemists, material scientists, physicists, mathematicians and computer scientists, joining forces to contribute to the exciting hot topic of progressing machine learning and AI for molecules and materials. The book that has emerged from a series of workshops provides a snapshot of this rapidly developing field. It contains tutorial material explaining the relevant foundations needed in chemistry, physics as well as machine learning to give an easy starting point for interested readers. In addition, a number of research papers defining the current state-of-the-art are included. The book has five parts (Fundamentals, Incorporating Prior Knowledge, Deep Learning of Atomistic Representations, Atomistic Simulations and Discovery and Design), each prefaced by editorial commentary that puts the respective parts into a broader scientific context. .

Machine learning. --- Quantum physics. --- Physics. --- Chemistry, Physical and theoretical. --- Quantum Physics. --- Numerical and Computational Physics, Simulation. --- Machine Learning. --- Theoretical and Computational Chemistry. --- Learning, Machine --- Artificial intelligence --- Machine theory --- Chemistry, Theoretical --- Physical chemistry --- Theoretical chemistry --- Chemistry --- Natural philosophy --- Philosophy, Natural --- Physical sciences --- Dynamics --- Quantum dynamics --- Quantum mechanics --- Quantum physics --- Physics --- Mechanics --- Thermodynamics --- Quantum theory.

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