Applied math and machine learning basics. Linear algebra -- Probability and information theory -- Numerical computation -- Machine learning basics -- Deep networks: modern practices. Deep feedforward networks -- Regularization for deep learning -- Optimization for training deep models -- Convolutional networks -- Sequence modeling: recurrent and recursive nets -- Practical methodology -- Applications -- Deep learning research. Linear factor models -- Autoencoders -- Representation learning -- Structured probabilistic models for deep learning -- Monte Carlo methods -- Confronting the partition function -- Approximate inference -- Deep generative models
The Deep Learning Predicting Accidents project was completed during the Spring 2020 semester as part of the Computer Science capstone course CS 4624: Multimedia, Hypertext, and Information Access. The goal of the project was to create a deep learning model of highway traffic dynamics that lead to car crashes, and make predictions as to whether a car crash has occurred given a particular traffic scenario. The intended use of this project is to improve the management and response times of Emergency Medical Technicians so as to maximize the survivability of highway car crashes. Predicting the occurrence of a highway car accident any significant length of time into the future is obviously not feasible, since the vast majority of crashes ultimately occur due to unpredictable human negligence and/or error. Therefore, we focused on identifying patterns in traffic speed, traffic flow, and weather that are conducive to the occurrence of car crashes, and using anomalies in these patterns to detect the occurrence of an accident. This projects model relies on: traffic speed, which is the average speed of highway traffic at a certain location and time; traffic flow, which is a measure of total traffic volume at a certain location and time that takes into account speed and number of cars; and the weather at all of these locations and times. We train and evaluate using traffic incident data, which contains information about car crashes on all California interstate highways. This data is obtained from government sources. The relevant data for this project is stored in a SQLite database, and both the code for data organization and preprocessing, as well as the deep learning model, are written in Python. The source code for the project is available at https://github.com/Elias222/DeepLearningPredictingAccidents. ; DeepLearningPredictingAccidentsPresentation.pdf - PDF file containing the final presentation given in class DeepLearningPredictingAccidentsPresentation.pptx- PowerPoint pptx file containing the final presentation given in class DeepLearningPredictingAccidentsReport.pdf- PDF file containing the final report for the project DeepLearningPredictingAccidentsReport.docx- Word docx file containing the final report for the project
AbstractMachine-generated artworks are now part of the contemporary art scene: they are attracting significant investments and they are presented in exhibitions together with those created by human artists. These artworks are mainly based on generative deep learning (GDL) techniques, which have seen a formidable development and remarkable refinement in the very recent years. Given the inherent characteristics of these techniques, a series of novel legal problems arise. In this article, we consider a set of key questions in the area of GDL for the arts, including the following: is it possible to use copyrighted works as training set for generative models? How do we legally store their copies in order to perform the training process? Who (if someone) will own the copyright on the generated data? We try to answer these questions considering the law in force in both the United States and the European Union, and potential future alternatives. We then extend our analysis to code generation, which is an emerging area of GDL. Finally, we also formulate a set of practical guidelines for artists and developers working on deep learning generated art, as well as some policy suggestions for policymakers.
Deep Learning - eine Schlüsseltechnologie der Künstlichen Intelligenz. Neuronale Netze bringen Höchstleistung, wenn sie zu Deep-Learning-Modellen verknüpft werden - vorausgesetzt, Sie machen es richtig. Große und gute Trainingsdaten beschaffen, geschickt implementieren ... lernen Sie hier, wie Sie die mächtige Technologie wirklich in Ihren Dienst nehmen. Unsere Autoren zeigen Ihnen sowohl die Arbeit mit Python und Keras als auch für den Browser mit JavaScript, HTML5 und TensorFlow.js.
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Systems medicine (SM) has emerged as a powerful tool for studying the human body at the systems level with the aim of improving our understanding, prevention and treatment of complex diseases. Being able to automatically extract relevant features needed for a given task from high-dimensional, heterogeneous data, deep learning (DL) holds great promise in this endeavour. This review paper addresses the main developments of DL algorithms and a set of general topics where DL is decisive, namely, within the SM landscape. It discusses how DL can be applied to SM with an emphasis on the applications to predictive, preventive and precision medicine. Several key challenges have been highlighted including delivering clinical impact and improving interpretability. We used some prototypical examples to highlight the relevance and significance of the adoption of DL in SM, one of them is involving the creation of a model for personalized Parkinson's disease. The review offers valuable insights and informs the research in DL and SM. ; This publication is based upon work from COST Action Open Multiscale Systems Medicine (OpenMultiMed, CA15120), supported by COST (European Cooperation in Science and Technology). COST is funded by the Horizon 2020 Framework Programme of the European Union. HZ and HYW are also supported by the MetaPlat(690998), SenseCare(690862) and STOP(823978) projects funded by H2020 RISE programme. FC and PT acknowledge the support of H2020 project iPC "individualized Paediatric Cure" (826121). Participation of V.S. in OpenMultiMed is supported by the Czech Ministry of Education, Youth and Sports (project LTC18074). JLM. thanks Escola Superior de Tecnologia e Gestão, Instituto Politécnico de Portalegre (ESTG/IPP); and Centro de Recursos Naturais e Ambiente, Instituto Superior Técnico (CERENA/IST) within the support of FCT-Fundação para a Ciência e a Tecnologia through the strategic project FCT-UIDB/04028/2020. MZ acknowledges the Spanish State Research Agency, through the Severo Ochoa and María de Maeztu Program for Centers and Units of Excellence in R&D (MDM-2017-0711) and the funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (851255). The Northern Ireland Centre for Stratified Medicine has been financed by a grant awarded to AJ Bjourson under the European Union Regional Development Fund (ERDF) EU Sustainable Competitiveness Programme for Northern Ireland & the Northern Ireland Public Health Agency (HSC R&D). TSR also acknowledges funding from PHA R&D Division and the Western Health & Social Care. ; Peer reviewed
In: Paper Accepted for Presentation at the 2018 Armed Forces Communications and Electronics Association (AFCEA) C4I and Cyber Conference, Erie Canal Chapter, New York, June 19 & 20, 2018.
In the last decade, there has been a significant increase in the number of papers related to machine learning and the application of machine learning in various fields of science. Belmonte et al. observed that between 2010 and 2018, the growth in the number of papers related to machine learning topics and big data was exponential. They analysed 4240 scientific publications from the Web of Science citation database [1]. Xu et al., in the analysis of publications in the International Journal of Machine Learning and Cybernetics, noted that from 2010 to 2017, the number of publications, the cooperation rate, the total number of authors, and the degree of cooperation had shown an increasing trend [2]. Dokic et al. analysed the publication of papers in which deep learning is applied in the field of agriculture and noticed that the first papers were published in 2014, and in the second half of the second decade of the 21st century, exponential growth in the number of published papers was observed [3]. The objectives of this paper are primarily to analyze the literature related to the application of deep learning in apple growing, to propose the division of these papers depending on the area, and to analyze the observed trends in publishing papers related to this topic. In the analysis, only papers published in scientific journals were considered, and the condition is that they be found in the citation databases of the Web of Science or Scopus. The second section gives a brief overview of deep learning and its development and a presentation of the importance of apple growing in agriculture. The third section is an overview of papers that use deep learning methods and solve some problems in growing apples. The third section is divided into four parts, depending on the area the paper deals with. The fourth section is a discussion and conclusion.
