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[null,"1.","Record Nr.",null,"UNINA9910270877103321"]
[null,null,"Autore",null,"Kent Janis"]
[null,null,"Titolo",null,"ADA in details : interpreting the 2010 Americans with Disabilities Act Standards for Accessible Design / / Janis Kent"]
[null,null,"Pubbl/distr/stampa",null,"Hoboken, New Jersey : , : Wiley, , 2017"]
[{"$nulls":4},"©2017"]
[null,null,"ISBN",null,"1-119-27760-4"]
[{"$nulls":4},"1-119-27759-0"]
[{"$nulls":4},"1-119-27761-2"]
[null,null,"Descrizione fisica",null,"1 online resource (299 pages) : illustrations (some color)"]
[null,null,"Collana",null,"THEi Wiley ebooks"]
[null,null,"Disciplina",null,"720.87"]
[null,null,"Soggetti",null,"Barrier-free design - United States"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Includes index."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,null,"Sommario/riassunto","Integrate your designs with compliant access interpretations ADA in Details provides a visual interpretation of the 2010 Americans with Disabilities Act (ADA) Standards for a convenient, go-to reference of pertinent scoping, technical requirements, and sourcing information. Architects, designers, and everyone else involved in the built environment can turn to this authoritative resource to understand accessibility compliance for places of public accommodation, commercial facilities, and public buildings. Every detail is presented with both a clear explanation and illustrations that synthesize federal regulations and the 2016 California Building Code (CBC)."]
[null,"2.","Record Nr.",null,"UNINA9910830558303321"]
[null,null,"Autore",null,"Al-Malah Kamal I. M."]
[null,null,"Titolo",null,"Machine and Deep Learning Using MATLAB : Algorithms and Tools for Scientists and Engineers / / Kamal I. M. Al-Malah"]
[null,null,"Pubbl/distr/stampa",null,"Hoboken, NJ : , : John Wiley & Sons, Inc., , [2024]"]
[{"$nulls":4},"©2024"]
[null,null,"ISBN",null,"9781394209101"]
[null,null,"Edizione",null,"[First edition.]"]
[null,null,"Descrizione fisica",null,"1 online resource (965 pages)"]
[null,null,"Disciplina",null,"001.642"]
[null,null,"Soggetti",null,"Machine learning"]
[{"$nulls":4},"Numerical analysis - Data processing"]
[{"$nulls":4},"Computer programming"]
[{"$nulls":4},"Numerical analysis - Computer programs"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Includes index."]
[null,null,null,"Sommario/riassunto","MACHINE AND DEEP LEARNING In-depth resource covering machine and deep learning methods using MATLAB tools and algorithms, providing insights and algorithmic decision-making processes Machine and Deep Learning Using MATLAB introduces early career professionals to the power of MATLAB to explore machine and deep learning applications by explaining the relevant MATLAB tool or app and how it is used for a given method or a collection of methods. Its properties, in terms of input and output arguments, are explained, the limitations or applicability is indicated via an accompanied text or a table, and a complete running example is shown with all needed MATLAB command prompt code. The text also presents the results, in the form of figures or tables, in parallel with the given MATLAB code, and the MATLAB written code can be later used as a template for trying to solve new cases or datasets. Throughout, the text features worked examples in each chapter for self-study with an accompanying website providing solutions and coding samples. Highlighted notes draw the attention of the user to critical points or issues. Readers will also find information on: Numeric data acquisition and analysis in the form of applying computational algorithms to predict the numeric data patterns (clustering or unsupervised learning) Relationships between predictors and response variable (supervised), categorically sub-divided into "]
[{"$nulls":4},"classification (discrete response) and regression (continuous response) Image acquisition and analysis in the form of applying one of neural networks, and estimating net accuracy, net loss, and/or RMSE for the successive training, validation, and testing steps Retraining and creation for image labeling, object identification, regression classification, and text recognition Machine and Deep Learning Using MATLAB is a useful and highly comprehensive resource on the subject for professionals, advanced students, and researchers who have some familiarity with MATLAB and are situated in engineering and scientific fields, who wish to gain mastery over the software and its numerous applications."]