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This is the first rigorous, self-contained treatment of the theory of deep learning. Starting with the foundations of the theory and building it up, this is essential reading for any scientists, instructors, and students interested in artificial intelligence and deep learning. It provides guidance on how to think about scientific questions, and leads readers through the history of the field and its fundamental connections to neuroscience. The author discusses many applications to beautiful problems in the natural sciences, in physics, chemistry, and biomedicine. Examples include the search for exotic particles and dark matter in experimental physics, the prediction of molecular properties and reaction outcomes in chemistry, and the prediction of protein structures and the diagnostic analysis of biomedical images in the natural sciences. The text is accompanied by a full set of exercises at different difficulty levels and encourages out-of-the-box thinking.

Machine learning. --- Science --- Natural science --- Natural sciences --- Science of science --- Sciences --- Learning, Machine --- Artificial intelligence --- Machine theory --- Technological innovations.

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Stochastic processes --- Molecular biology --- Artificial intelligence. Robotics. Simulation. Graphics --- -Molecular biology --- -Neural networks (Computer science) --- Artificial neural networks --- Nets, Neural (Computer science) --- Networks, Neural (Computer science) --- Neural nets (Computer science) --- Natural computation --- Soft computing --- Molecular biochemistry --- Systems biology --- Analysis, Markov --- Chains, Markov --- Markoff processes --- Markov analysis --- Markov chains --- Markov models --- Models, Markov --- Processes, Markov --- Learning, Machine --- Machine theory --- Learning: analogies concept learning induction knowledge acquisition language acquisition parameter learning (Artificial intelligence)--See also {681.3*K32} --- Bioinformatics. --- Markov processes. --- Neural networks (Computer science). --- 681.3*I26 Learning: analogies concept learning induction knowledge acquisition language acquisition parameter learning (Artificial intelligence)--See also {681.3*K32} --- Machine learning --- Markov processes --- Neural networks (Computer science) --- 681.3*I26 --- Artificial intelligence --- 681.3*I26 Learning: analogies; concept learning; induction; knowledge acquisition; language acquisition; parameter learning (Artificial intelligence)--See also {681.3*K32} --- Learning: analogies; concept learning; induction; knowledge acquisition; language acquisition; parameter learning (Artificial intelligence)--See also {681.3*K32} --- Molecular biophysics --- Biochemistry --- Biophysics --- Biomolecules --- Computer simulation --- Mathematical models --- Computer. Automation --- Machine learning. --- Computer simulation. --- Mathematical models. --- 57.087 --- 57.087 Methods and techniques for parameter estimation. Recording of biological data --- Methods and techniques for parameter estimation. Recording of biological data --- Computersimulation ; SWD-ID: 41482591 --- Maschinelles Lernen ; SWD-ID: 41937545 --- Molekularbiologie ; SWD-ID: 40399837 --- Neuronales Netz ; SWD-ID: 42261272 --- Molecular biology - Computer simulation --- Molecular biology - Mathematical models --- Molecular biology - computer simulation --- Molecular biomogy - mathematical models

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Massive data acquisition technologies, such as genome sequencing, high-throughput drug screening, and DNA arrays are in the process of revolutionizing biology and medicine. Using the mRNA of a given cell, at a given time, under a given set of conditions, DNA microarrays can provide a snapshot of the level of expression of all the genes in the cell. Such snapshots can be used to study fundamental biological phenomena such as development or evolution, to determine the function of new genes, to infer the role individual genes or groups of genes may play in diseases, and to monitor the effect of drugs and other compounds on gene expression. Originally published in 2002, this inter-disciplinary introduction to DNA arrays will be of value to anyone with an a interest in this powerful technology.

DNA microarrays. --- Gene expression. --- Genes --- Genetic regulation --- DNA biochips --- Microarrays, DNA --- Biochips --- Immobilized nucleic acids --- Expression