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Programming (Mathematics) --- MATLAB. --- Engineering mathematics --- Numerical analysis --- Programming Languages. --- Numerical Analysis, Computer-Assisted. --- Software. (DNLM)D012984 --- Data processing. --- Computer programs. --- Analysis, Computer-Assisted Numerical --- Computer-Assisted Numerical Analysis --- Analyses, Computer-Assisted Numerical --- Analysis, Computer Assisted Numerical --- Computer Assisted Numerical Analysis --- Computer-Assisted Numerical Analyses --- Numerical Analyses, Computer-Assisted --- Numerical Analysis, Computer Assisted --- Language, Programming --- Languages, Programming --- Programming Language --- Mathematical programming --- Goal programming --- Algorithms --- Functional equations --- Mathematical optimization --- Operations research --- MATLAB (Computer program) --- Matrix laboratory --- MATLAB (Computer file) --- Software.

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"Using R at the Bench: Step-by-Step Data Analytics for Biologists is a convenient bench-side handbook for biologists, designed as a handy reference guide for elementary and intermediate statistical analyses using the free/public software package known as "R." The expectations for biologists to have a more complete understanding of statistics are growing rapidly. New technologies and new areas of science, such as microarrays, next-generation sequencing, and proteomics, have dramatically increased the need for quantitative reasoning among biologists when designing experiments and interpreting results. Even the most routine informatics tools rely on statistical assumptions and methods that need to be appreciated if the scientific results are to be correct, understood, and exploited fully. Although the original Statistics at the Bench is still available for sale and has all examples in Excel, this new book uses the same text and examples in R.A new chapter introduces the basics of R: where to download, how to get started, and some basic commands and resources. There is also a new chapter that explains how to analyze next-generation sequencing data using R (specifically, RNA-Seq). R is powerful statistical software with many specialized packages for biological applications and Using R at the Bench: Step-by-Step Data Analytics for Biologists is an excellent resource for those biologists who want to learn R. This handbook for working scientists provides a simple refresher for those who have forgotten what they once knew and an overview for those wishing to use more quantitative reasoning in their research. Statistical methods, as well as guidelines for the interpretation of results, are explained using simple examples. Throughout the book, examples are accompanied by detailed R commands for easy reference."--Publisher's description.

Bioinformatics --- Biology --- R (Computer program language) --- Computational Biology --- Statistics as Topic. --- Programming Languages. --- GNU-S (Computer program language) --- Domain-specific programming languages --- Language, Programming --- Languages, Programming --- Programming Language --- Area Analysis --- Estimation Technics --- Estimation Techniques --- Indirect Estimation Technics --- Indirect Estimation Techniques --- Multiple Classification Analysis --- Service Statistics --- Statistical Study --- Statistics, Service --- Tables and Charts as Topic --- Analyses, Area --- Analyses, Multiple Classification --- Area Analyses --- Classification Analyses, Multiple --- Classification Analysis, Multiple --- Estimation Technic, Indirect --- Estimation Technics, Indirect --- Estimation Technique --- Estimation Technique, Indirect --- Estimation Techniques, Indirect --- Indirect Estimation Technic --- Indirect Estimation Technique --- Multiple Classification Analyses --- Statistical Studies --- Studies, Statistical --- Study, Statistical --- Technic, Indirect Estimation --- Technics, Estimation --- Technics, Indirect Estimation --- Technique, Estimation --- Technique, Indirect Estimation --- Techniques, Estimation --- Techniques, Indirect Estimation --- Bio-Informatics --- Biology, Computational --- Computational Molecular Biology --- Molecular Biology, Computational --- Bio Informatics --- Bio-Informatic --- Bioinformatic --- Biologies, Computational Molecular --- Biology, Computational Molecular --- Computational Molecular Biologies --- Molecular Biologies, Computational --- Computational Chemistry --- Genomics --- Bio-informatics --- Biological informatics --- Information science --- Computational biology --- Systems biology --- Data processing --- Statistics as Topic --- Programming Languages

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Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. The book builds your understanding of deep learning through intuitive explanations and practical examples.

Artificial intelligence. --- Computer vision. --- Machine learning --- Mathematical statistics --- Neural networks (Computer science). --- R (Computer program language). --- Technological innovations. --- Data processing. --- Artificial intelligence. Robotics. Simulation. Graphics --- Mathematical linguistics --- Neural networks (Computer science) --- R (Computer program language) --- Artificial intelligence --- Computer vision --- Programming Languages --- Artificial Intelligence --- Neural Networks, Computer --- Artificial neural networks --- Nets, Neural (Computer science) --- Networks, Neural (Computer science) --- Neural nets (Computer science) --- Natural computation --- Soft computing --- GNU-S (Computer program language) --- Domain-specific programming languages --- Technological innovations --- Data processing --- Computational Neural Networks --- Connectionist Models --- Models, Neural Network --- Neural Network Models --- Neural Networks (Computer) --- Perceptrons --- Computational Neural Network --- Computer Neural Network --- Computer Neural Networks --- Connectionist Model --- Model, Connectionist --- Model, Neural Network --- Models, Connectionist --- Network Model, Neural --- Network Models, Neural --- Network, Computational Neural --- Network, Computer Neural --- Network, Neural (Computer) --- Networks, Computational Neural --- Networks, Computer Neural --- Networks, Neural (Computer) --- Neural Network (Computer) --- Neural Network Model --- Neural Network, Computational --- Neural Network, Computer --- Neural Networks, Computational --- Perceptron --- 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, Artificial --- 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 --- Language, Programming --- Languages, Programming --- Programming Language --- Machine vision --- Vision, Computer --- Image processing --- Pattern recognition systems --- Learning, Machine --- Machine theory --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- #SBIB:303H4 --- Informatica in de sociale wetenschappen