{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":2,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":28,"n_cols":5,"rows_kept":28,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[2,4,2,5],[3,2,3,3],[5,2,5,3],[6,2,6,3],[7,2,7,3],[8,2,8,3],[9,2,9,3],[10,2,10,3],[11,2,11,3],[12,2,12,3],[14,2,14,3],[16,2,16,3],[16,4,16,5],[17,2,17,3],[17,4,17,5],[18,2,18,3],[18,4,18,5],[19,2,19,3],[20,2,20,3],[21,2,21,3],[22,2,22,3],[23,2,23,3],[24,2,24,3],[25,2,25,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNINA9910484054903321"]
[null,null,"Autore",null,"Bhattacharya Shuvajit"]
[null,null,"Titolo",null,"A primer on machine learning in subsurface geosciences / / Shuvajit Bhattacharya"]
[null,null,"Pubbl/distr/stampa",null,"Cham, Switzerland : , : Springer, , [2021]"]
[{"$nulls":4},"©2021"]
[null,null,"ISBN",null,"3-030-71768-2"]
[null,null,"Edizione",null,"[1st ed. 2021.]"]
[null,null,"Descrizione fisica",null,"1 online resource (XVII, 172 p. 130 illus., 118 illus. in color.)"]
[null,null,"Collana",null,"SpringerBriefs in Petroleum Geoscience & Engineering, , 2509-3126"]
[null,null,"Disciplina",null,"550.285"]
[null,null,"Soggetti",null,"Geology - Data processing"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Introduction -- Brief Review of Statistical Measures -- Basic Steps in Machine Learning and Deep Learning Models -- Brief Review of Popular Machine Learning and Deep Learning Algorithms -- Applications of ML/DL in Geophysics and Petrophysics Domain -- Applications of ML/DL in Geology Domain -- Multi-scale Data Integration and Analytics -- The Road Ahead."]
[null,null,null,"Sommario/riassunto","This book provides readers with a timely review and discussion of the success, promise, and perils of machine learning in geosciences. It explores the fundamentals of data science and machine learning, and how their advances have disrupted the traditional workflows used in the industry and academia, including geology, geophysics, petrophysics, geomechanics, and geochemistry. It then presents the real-world applications and explains that, while this disruption has affected the top-level executives, geoscientists as well as field operators in the industry and academia, machine learning will ultimately benefit these users. The book is written by a practitioner of machine learning and statistics, keeping geoscientists in mind. It highlights the need to go beyond concepts covered in STAT 101 courses and embrace new computational tools to solve complex problems in geosciences. It also offers practitioners, researchers, and academics insights into how to identify, develop, deploy, and recommend fit-for-purpose machine learning models to solve real-world problems in subsurface geosciences. ."]
[null,"2.","Record Nr.",null,"UNINA9910557278903321"]
[null,null,"Autore",null,"Costa Antonio Roberto Faustino da"]
[null,null,"Titolo",null,"Industrialização do ensino e política de educação a distância"]
[null,null,"Pubbl/distr/stampa",null,"SciELO Books - EDUEPB, 2019"]
[null,null,"ISBN",null,"85-7879-350-1"]
[null,null,"Descrizione fisica",null,"1 online resource (362 p.)"]
[null,null,"Collana",null,"Ensino e aprendizagem"]
[null,null,"Soggetti",null,"Open learning, home learning, distance education"]
[null,null,"Lingua di pubblicazione",null,"Portoghese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Sommario/riassunto","O livro Industrialização do ensino e política de educação a distância coloca-se como um trabalho sério e rigoroso ao aceitar o desafio de afirmar que \"o discurso da industrialização do ensino governa a prática discursiva que norteia a política nacional de educação a distância\". O autor recorre a três ideologias que deram origem a diferentes teorias, concepções, abordagens e modelos de educação a distância: a \"teoria da interdependência e autonomia\", a \"teoria da comunicação e interação\" e a \"teoria da industrialização do ensino\". A partir desta última, faz um exame da política nacional de EAD, para mostrar que a \"abordagem parte da premissa de que, enquanto a educação face a face compreende formas pré-industriais, a educação a distância pressupõe essencialmente condições industriais de ensino\", as quais teriam \"como base o princípio da racionalização\" que afeta o espírito, a letra e a política do processo de ensino-aprendizagem."]