An aproach to a study of physician learning patterns

1984 ◽  
Vol 4 (4) ◽  
pp. 48-50
Author(s):  
Linda J. Hummel
Keyword(s):  
Learning Patterns ◽  
Physician Learning

Deep Learning in the Study of Protein-Related Interactions

2020 ◽  
Vol 27 (5) ◽  
pp. 359-369 ◽  
Author(s):  
Cheng Shi ◽  
Jiaxing Chen ◽  
Xinyue Kang ◽  
Guiling Zhao ◽  
Xingzhen Lao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Protein Interactions ◽  
Physiological Data ◽  
Great Promise ◽  
Complex Data ◽  
Protein Protein Interactions ◽  
Learning Patterns ◽  
Introductory Overview ◽  
Protein Research ◽  
Neural Network Theory

: Protein-related interaction prediction is critical to understanding life processes, biological functions, and mechanisms of drug action. Experimental methods used to determine proteinrelated interactions have always been costly and inefficient. In recent years, advances in biological and medical technology have provided us with explosive biological and physiological data, and deep learning-based algorithms have shown great promise in extracting features and learning patterns from complex data. At present, deep learning in protein research has emerged. In this review, we provide an introductory overview of the deep neural network theory and its unique properties. Mainly focused on the application of this technology in protein-related interactions prediction over the past five years, including protein-protein interactions prediction, protein-RNA\DNA, Protein– drug interactions prediction, and others. Finally, we discuss some of the challenges that deep learning currently faces.

scholarly journals A Resource-Allocating Network for Function Interpolation

1991 ◽  
Vol 3 (2) ◽  
pp. 213-225 ◽  
Author(s):  
John Platt
Keyword(s):  
Time Series ◽  
Learning Process ◽  
Gradient Descent ◽  
Chaotic Time Series ◽  
Local Region ◽  
Unusual Pattern ◽  
Network Parameters ◽  
Learning Patterns ◽  
Compact Representations

We have created a network that allocates a new computational unit whenever an unusual pattern is presented to the network. This network forms compact representations, yet learns easily and rapidly. The network can be used at any time in the learning process and the learning patterns do not have to be repeated. The units in this network respond to only a local region of the space of input values. The network learns by allocating new units and adjusting the parameters of existing units. If the network performs poorly on a presented pattern, then a new unit is allocated that corrects the response to the presented pattern. If the network performs well on a presented pattern, then the network parameters are updated using standard LMS gradient descent. We have obtained good results with our resource-allocating network (RAN). For predicting the Mackey-Glass chaotic time series, RAN learns much faster than do those using backpropagation networks and uses a comparable number of synapses.

Is It a Pattern?

2013 ◽  
Vol 19 (9) ◽  
pp. 564-571 ◽  
Author(s):  
Lynn M. McGarvey
Keyword(s):  
Decision Making ◽  
Teaching And Learning ◽  
Pattern Identification ◽  
Early Years ◽  
Learning Patterns

A child's decision-making photo activity about pattern identification presents implications for teaching and learning patterns in the early years.

Pengembangan Model Aktualisasi Kepramukaan pada Pembelajaran Bahasa Indonesia di SMA

2021 ◽  
Vol 14 (1) ◽  
pp. 66
Author(s):  
Achmad Irfandi ◽  
Bambang Yulianto ◽  
Maria Mintowati
Keyword(s):  
Qualitative Methods ◽  
Language Learning ◽  
Meaningful Learning ◽  
Learning Patterns ◽  
Bahasa Indonesia

Development of Scouting Actualization Model in Indonesian Language Learning in Senior High SchoolABSTRAKPenelitian ini bertujuan untuk mengembangkan pembelajaran Bahasa Indonesia dengan menggunakan Model Aktualisasi Kepramukaan. Model Aktualisasi Kepramukaan hadir dalam Permendikbud nomor 63 tahun 2014 tentang Ekstrakurikuler Wajib Pendidikan Kepramukaan yang memuat integrasi muatan KI 1 hingga KI 4 dalam kegiatan Kepramukaan.  Penelitian ini menerapkan metode kualitatif dengan validasi menggunakan instrumen angket kepada beberapa validator ahli. Selain itu, dalam penerapannya juga diukur keefektifan dan kepraktisannya. Kevalidan model ini mendapat nilai 87% (berkategori sangat baik). Penelitian ini menemukan bahwa model aktualisasi kepramukaan dapat menghasilkan pola belajar yang berbeda dan tidak membosankan bagi siswa. Model ini juga memberikan pembelajaran yang bermakna bagi siswa.Kata kunci: Aktualisasi, Bahasa, Siswa SMAABSTRACTThis study aims to develop Indonesian language learning using the Scouting Actualization Model. The Scouting Actualization Model is in Permendikbud number 63 of 2014 concerning Scouting Education Compulsory Extracurricular which includes the integration from KI 1 to KI 4 content in Scouting activities. This research applies qualitative methods with validation using a questionnaire instrument to several expert validators. In addition, its effectiveness and practicality are also measured in its application. The validity of this model gets a score of 87% (categorized as very good). This study found that the scouting actualization model can produce different learning patterns and is not boring for students. This model also provides meaningful learning for students.Keywords: Actualization, Language, Senior High Schol Student

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