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Relevant bibliographies by topics / Deep machine learning / Dissertations / Theses

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Dissertations / Theses on the topic 'Deep machine learning'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

He, Fengxiang. "Theoretical Deep Learning." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/25674.

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Deep learning has long been criticised as a black-box model for lacking sound theoretical explanation. During the PhD course, I explore and establish theoretical foundations for deep learning. In this thesis, I present my contributions positioned upon existing literature: (1) analysing the generalizability of the neural networks with residual connections via complexity and capacity-based hypothesis complexity measures; (2) modeling stochastic gradient descent (SGD) by stochastic differential equations (SDEs) and their dynamics, and further characterizing the generalizability of deep learning;

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2

Fan, Shuangfei. "Deep Representation Learning on Labeled Graphs." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/96596.

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We introduce recurrent collective classification (RCC), a variant of ICA analogous to recurrent neural network prediction. RCC accommodates any differentiable local classifier and relational feature functions. We provide gradient-based strategies for optimizing over model parameters to more directly minimize the loss function. In our experiments, this direct loss minimization translates to improved accuracy and robustness on real network data. We demonstrate the robustness of RCC in settings where local classification is very noisy, settings that are particularly challenging for ICA. As a new

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3

Zhuang, Zhongfang. "Deep Learning on Attributed Sequences." Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-dissertations/507.

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Recent research in feature learning has been extended to sequence data, where each instance consists of a sequence of heterogeneous items with a variable length. However, in many real-world applications, the data exists in the form of attributed sequences, which is composed of a set of fixed-size attributes and variable-length sequences with dependencies between them. In the attributed sequence context, feature learning remains challenging due to the dependencies between sequences and their associated attributes. In this dissertation, we focus on analyzing and building deep learning models for

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FRACCAROLI, MICHELE. "Explainable Deep Learning." Doctoral thesis, Università degli studi di Ferrara, 2023. https://hdl.handle.net/11392/2503729.

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Il grande successo che il Deep Learning ha ottenuto in ambiti strategici per la nostra società quali l'industria, la difesa, la medicina etc., ha portanto sempre più realtà a investire ed esplorare l'utilizzo di questa tecnologia. Ormai si possono trovare algoritmi di Machine Learning e Deep Learning quasi in ogni ambito della nostra vita. Dai telefoni, agli elettrodomestici intelligenti fino ai veicoli che guidiamo. Quindi si può dire che questa tecnologia pervarsiva è ormai a contatto con le nostre vite e quindi dobbiamo confrontarci con essa. Da questo nasce l’eXplainable Artificial Intelli

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5

Riva, Mateus. "Spatial Relational Reasoning in Machine Learning : Deep Learning and Graph Clustering." Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAT043.

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Cette thèse étudie les capacités des méthodes d'apprentissage automatique à raisonner sur des relations spatiales, en particulier sur les relations directionnelles, et l'utilisation de connaissances relationnelles, connues a priori, par ces méthodes. Il existe de nombreux travaux dans le domaine de l'exploitation de connaissances sur les relations dans des méthodes d'apprentissage automatique. Cependant, ce corpus de travaux laisse encore plusieurs questions ouvertes. Tout au long de cette thèse, nous explorons, étudions et tentons d'expliquer différentes questions de recherche liées à ces que

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Elmarakeby, Haitham Abdulrahman. "Deep Learning for Biological Problems." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/86264.

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The last decade has witnessed a tremendous increase in the amount of available biological data. Different technologies for measuring the genome, epigenome, transcriptome, proteome, metabolome, and microbiome in different organisms are producing large amounts of high-dimensional data every day. High-dimensional data provides unprecedented challenges and opportunities to gain a better understanding of biological systems. Unlike other data types, biological data imposes more constraints on researchers. Biologists are not only interested in accurate predictive models that capture complex input-out

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7

Arnold, Ludovic. "Learning Deep Representations : Toward a better new understanding of the deep learning paradigm." Phd thesis, Université Paris Sud - Paris XI, 2013. http://tel.archives-ouvertes.fr/tel-00842447.

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Since 2006, deep learning algorithms which rely on deep architectures with several layers of increasingly complex representations have been able to outperform state-of-the-art methods in several settings. Deep architectures can be very efficient in terms of the number of parameters required to represent complex operations which makes them very appealing to achieve good generalization with small amounts of data. Although training deep architectures has traditionally been considered a difficult problem, a successful approach has been to employ an unsupervised layer-wise pre-training step to init

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8

Padarian, Campusano Jose Sergei. "Machine learning to generate soil information." Thesis, The University of Sydney, 2019. https://hdl.handle.net/2123/22081.

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This thesis is concerned with the novel use of machine learning (ML) methods in soil science research. ML adoption in soil science has increased considerably, especially in pedometrics (the use of quantitative methods to study the variation of soils). In parallel, the size of the soil datasets has also increased thanks to projects of global impact that aim to rescue legacy data or new large extent surveys to collect new information. While we have big datasets and global projects, currently, modelling is mostly based on "traditional" ML approaches which do not take full advantage of these large

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9

Shi, Shaohuai. "Communication optimizations for distributed deep learning." HKBU Institutional Repository, 2020. https://repository.hkbu.edu.hk/etd_oa/813.

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With the increasing amount of data and the growing computing power, deep learning techniques using deep neural networks (DNNs) have been successfully applied in many practical artificial intelligence applications. The mini-batch stochastic gradient descent (SGD) algorithm and its variants are the most widely used algorithms in training deep models. The SGD algorithm is an iterative algorithm that needs to update the model parameters many times by traversing the training data, which is very time-consuming even using the single powerful GPU or TPU. Therefore, it becomes a common practice to expl

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10

Tegendal, Lukas. "Watermarking in Audio using Deep Learning." Thesis, Linköpings universitet, Datorseende, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-159191.

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Watermarking is a technique used to used to mark the ownership in media such as audio or images by embedding a watermark, e.g. copyrights information, into the media. A good watermarking method should perform this embedding without affecting the quality of the media. Recent methods for watermarking in images uses deep learning to embed and extract the watermark in the images. In this thesis, we investigate watermarking in the hearable frequencies of audio using deep learning. More specifically, we try to create a watermarking method for audio that is robust to noise in the carrier, and that al

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11

Manda, Kundan Reddy. "Sentiment Analysis of Twitter Data Using Machine Learning and Deep Learning Methods." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18447.

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Background: Twitter, Facebook, WordPress, etc. act as the major sources of information exchange in today's world. The tweets on Twitter are mainly based on the public opinion on a product, event or topic and thus contains large volumes of unprocessed data. Synthesis and Analysis of this data is very important and difficult due to the size of the dataset. Sentiment analysis is chosen as the apt method to analyse this data as this method does not go through all the tweets but rather relates to the sentiments of these tweets in terms of positive, negative and neutral opinions. Sentiment Analysis

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12

Flowers, Bryse Austin. "Adversarial RFML: Evading Deep Learning Enabled Signal Classification." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/91987.

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Deep learning has become an ubiquitous part of research in all fields, including wireless communications. Researchers have shown the ability to leverage deep neural networks (DNNs) that operate on raw in-phase and quadrature samples, termed Radio Frequency Machine Learning (RFML), to synthesize new waveforms, control radio resources, as well as detect and classify signals. While there are numerous advantages to RFML, this thesis answers the question "is it secure?" DNNs have been shown, in other applications such as Computer Vision (CV), to be vulnerable to what are known as adversarial evasio

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13

Franch, Gabriele. "Deep Learning for Spatiotemporal Nowcasting." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/295096.

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Nowcasting – short-term forecasting using current observations – is a key challenge that human activities have to face on a daily basis. We heavily rely on short-term meteorological predictions in domains such as aviation, agriculture, mobility, and energy production. One of the most important and challenging task for meteorology is the nowcasting of extreme events, whose anticipation is highly needed to mitigate risk in terms of social or economic costs and human safety. The goal of this thesis is to contribute with new machine learning methods to improve the spatio-temporal precision of now

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Franch, Gabriele. "Deep Learning for Spatiotemporal Nowcasting." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/295096.

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Nowcasting – short-term forecasting using current observations – is a key challenge that human activities have to face on a daily basis. We heavily rely on short-term meteorological predictions in domains such as aviation, agriculture, mobility, and energy production. One of the most important and challenging task for meteorology is the nowcasting of extreme events, whose anticipation is highly needed to mitigate risk in terms of social or economic costs and human safety. The goal of this thesis is to contribute with new machine learning methods to improve the spatio-temporal precision of now

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Piano, Francesco. "Deep Reinforcement Learning con PyTorch." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25340/.

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Il Reinforcement Learning è un campo di ricerca del Machine Learning in cui la risoluzione di problemi da parte di un agente avviene scegliendo l’azione più idonea da eseguire attraverso un processo di apprendimento iterativo, in un ambiente dinamico che lo incentiva tramite ricompense. Il Deep Learning, anch’esso approccio del Machine Learning, sfruttando una rete neurale artificiale è in grado di applicare metodi di apprendimento per rappresentazione allo scopo di ottenere una struttura dei dati più idonea ad essere elaborata. Solo recentemente il Deep Reinforcement Learning, creato

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Mariani, Tommaso. "Deep reinforcement learning for industrial applications." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20548/.

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In recent years there has been a growing attention from the world of research and companies in the field of Machine Learning. This interest, thanks mainly to the increasing availability of large amounts of data, and the respective strengthening of the hardware sector useful for their analysis, has led to the birth of Deep Learning. The growing computing capacity and the use of mathematical optimization techniques, already studied in depth but with few applications due to a low computational power, have then allowed the development of a new approach called Reinforcement Learning. This thesis w

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Rigaki, Maria. "Adversarial Deep Learning Against Intrusion Detection Classifiers." Thesis, Luleå tekniska universitet, Datavetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-64577.

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Traditional approaches in network intrusion detection follow a signature-based ap- proach, however the use of anomaly detection approaches based on machine learning techniques have been studied heavily for the past twenty years. The continuous change in the way attacks are appearing, the volume of attacks, as well as the improvements in the big data analytics space, make machine learning approaches more alluring than ever. The intention of this thesis is to show that using machine learning in the intrusion detection domain should be accompanied with an evaluation of its robustness against adve

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Ida, Yasutoshi. "Algorithms for Accelerating Machine Learning with Wide and Deep Models." Doctoral thesis, Kyoto University, 2021. http://hdl.handle.net/2433/263771.

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DARAIO, ELENA. "Digging Deep Into Urban Mobility Data Through Machine Learning Techniques." Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2972557.

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Kostopouls, Theodore P. "A Machine Learning approach to Febrile Classification." Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-theses/1173.

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General health screening is needed to decrease the risk of pandemic in high volume areas. Thermal characterization, via infrared imaging, is an effective technique for fever detection, however, strict use requirements in combination with highly controlled environmental conditions compromise the practicality of such a system. Combining advanced processing techniques to thermograms of individuals can remove some of these requirements allowing for more flexible classification algorithms. The purpose of this research was to identify individuals who had febrile status utilizing modern thermal imagi

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Darborg, Alex. "Real-time face recognition using one-shot learning : A deep learning and machine learning project." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-40069.

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Face recognition is often described as the process of identifying and verifying people in a photograph by their face. Researchers have recently given this field increased attention, continuously improving the underlying models. The objective of this study is to implement a real-time face recognition system using one-shot learning. “One shot” means learning from one or few training samples. This paper evaluates different methods to solve this problem. Convolutional neural networks are known to require large datasets to reach an acceptable accuracy. This project proposes a method to solve this p

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Shao, Han. "Pretraining Deep Learning Models for Natural Language Understanding." Oberlin College Honors Theses / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=oberlin158955297757398.

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23

Jaderberg, Maxwell. "Deep learning for text spotting." Thesis, University of Oxford, 2015. http://ora.ox.ac.uk/objects/uuid:e893c11e-6b6b-4d11-bb25-846bcef9b13e.

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This thesis addresses the problem of text spotting - being able to automatically detect and recognise text in natural images. Developing text spotting systems, systems capable of reading and therefore better interpreting the visual world, is a challenging but wildly useful task to solve. We approach this problem by drawing on the successful developments in machine learning, in particular deep learning and neural networks, to present advancements using these data-driven methods. Deep learning based models, consisting of millions of trainable parameters, require a lot of data to train effectivel

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Zhao, Yajing. "Chaotic Model Prediction with Machine Learning." BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8419.

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Chaos theory is a branch of modern mathematics concerning the non-linear dynamic systems that are highly sensitive to their initial states. It has extensive real-world applications, such as weather forecasting and stock market prediction. The Lorenz system, defined by three ordinary differential equations (ODEs), is one of the simplest and most popular chaotic models. Historically research has focused on understanding the Lorenz system's mathematical characteristics and dynamical evolution including the inherent chaotic features it possesses. In this thesis, we take a data-driven approach and

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Geras, Krzysztof Jerzy. "Exploiting diversity for efficient machine learning." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/28839.

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A common practice for solving machine learning problems is currently to consider each problem in isolation, starting from scratch every time a new learning problem is encountered or a new model is proposed. This is a perfectly feasible solution when the problems are sufficiently easy or, if the problem is hard when a large amount of resources, both in terms of the training data and computation, are available. Although this naive approach has been the main focus of research in machine learning for a few decades and had a lot of success, it becomes infeasible if the problem is too hard in propor

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Al, Chalati Abdul Aziz, and Syed Asad Naveed. "Transfer Learning for Machine Diagnostics." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-43185.

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Fault detection and diagnostics are crucial tasks in condition-based maintenance. Industries nowadays are in need of fault identification in their machines as early as possible to save money and take precautionary measures in case of fault occurrence. Also, it is beneficial for the smooth interference in the manufacturing process in which it avoids sudden malfunctioning. Having sufficient training data for industrial machines is also a major challenge which is a prerequisite for deep neural networks to train an accurate prediction model. Transfer learning in such cases is beneficial as it can

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Morri, Francesco. "A thermodynamic approach to deep learning." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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Neural Networks are an incredibly powerful tool used to solve complex problems. The actual functioning of this tool and its behaviour when applied to different kind of problems is not completely explain though. In this work we study the behaviour of a neural network, used to classify images, through a physical model, based on statistical thermodynamics. We found interesting results regarding the temperature of the different components of the network, that may be exploited in a more efficient training algorithm.

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CATTANEO, DANIELE. "Machine Learning Techniques for Urban Vehicle Localization." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2020. http://hdl.handle.net/10281/263540.

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In questa tesi presento il mio lavoro di dottorato, che ha riguardato la localizzazione di un veicolo stradale in ambito urbano. In particolare, ho fatto uso di tecniche di Machine Learning per l'elaborazione delle immagini provenienti dalle camere a bordo di un veicolo. I sistemi sviluppati hanno lo scopo di produrre una stima della posa del veicolo e quindi, nel caso di deep neural networks, si tratta di reti che effettuano una pose regression. Al meglio della mia conoscenza, alcuni dei miei sviluppi sono i primi in letteratura in grado di effettuare visual pose regression basandosi su mappe

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Addis, Antonio. "Deep reinforcement learning optimization of video streaming." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.

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Questa tesi si occuperà dell'ottimizzazione delle performance di streaming video attraverso internet, divenute particolarmente problematiche con l'avvento delle nuove risoluzioni ultraHD e i video a 360 gradi per la realtà virtuale. Verranno confrontate le performance ottenute con gli algoritmi che attualmente fanno parte dello stato dell'arte, e sviluppato un modello di reinforcement learning che sia capace di effettuare scelte per migliorare la QoE(quality of experience) durante una sessione di streaming. Per i video a 360 gradi, verrà inoltre implementata la tecnica snapchange, con questo

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CAPUA, M. DI. "A DEEP LEARNING APPROACH FOR SENTIMENT ANALYSIS." Doctoral thesis, Università degli Studi di Milano, 2017. http://hdl.handle.net/2434/467844.

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La Sentiment Analysis si riferisce alla analisi qualitativa volta ad identificare e classificare opinioni contenute in frasi e testi, allo scopo di stabilire lo “stato d’animo” dell’autore rispetto ad un particolare argomento o prodotto, e di determinare se tale stato è di fatto positivo, negativo oppure neutrale. Le opinioni espresse in un testo, come ad esempio giudizi, sentimenti ed emozioni, sono di recente diventate oggetto di studio e di ricerca sia in ambito accademico che industriale. Sfortunatamente la comprensione del linguaggio, applicata a commenti di utenti, è un attività estremam

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Emenonye, Don-Roberts Ugochukwu. "Application of Machine Learning to Multi Antenna Transmission and Machine Type Resource Allocation." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/99956.

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Wireless communication systems is a well-researched area in electrical engineering that has continually evolved over the past decades. This constant evolution and development have led to well-formulated theoretical baselines in terms of reliability and efficiency. However, most communication baselines are derived by splitting the baseband communications into a series of modular blocks like modulation, coding, channel estimation, and orthogonal frequency modulation. Subsequently, these blocks are independently optimized. Although this has led to a very efficient and reliable process, a theoreti

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Mansanet, Sandín Jorge. "Contributions to Deep Learning Models." Doctoral thesis, Universitat Politècnica de València, 2016. http://hdl.handle.net/10251/61296.

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[EN] Deep Learning is a new area of Machine Learning research which aims to create computational models that learn several representations of the data using deep architectures. These methods have become very popular over the last few years due to the remarkable results obtained in speech recognition, visual object recognition, object detection, natural language processing, etc. The goal of this thesis is to present some contributions to the Deep Learning framework, particularly focused on computer vision problems dealing with images. These contributions can be summarized in two novel methods

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Kendall, Alex Guy. "Geometry and uncertainty in deep learning for computer vision." Thesis, University of Cambridge, 2019. https://www.repository.cam.ac.uk/handle/1810/287944.

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Deep learning and convolutional neural networks have become the dominant tool for computer vision. These techniques excel at learning complicated representations from data using supervised learning. In particular, image recognition models now out-perform human baselines under constrained settings. However, the science of computer vision aims to build machines which can see. This requires models which can extract richer information than recognition, from images and video. In general, applying these deep learning models from recognition to other problems in computer vision is significantly more

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Weideman, Ryan. "Robot Navigation in Cluttered Environments with Deep Reinforcement Learning." DigitalCommons@CalPoly, 2019. https://digitalcommons.calpoly.edu/theses/2011.

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The application of robotics in cluttered and dynamic environments provides a wealth of challenges. This thesis proposes a deep reinforcement learning based system that determines collision free navigation robot velocities directly from a sequence of depth images and a desired direction of travel. The system is designed such that a real robot could be placed in an unmapped, cluttered environment and be able to navigate in a desired direction with no prior knowledge. Deep Q-learning, coupled with the innovations of double Q-learning and dueling Q-networks, is applied. Two modifications of this a

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Pasa, Luca. "Linear Models and Deep Learning: Learning in Sequential Domains." Doctoral thesis, Università degli studi di Padova, 2017. http://hdl.handle.net/11577/3425865.

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With the diffusion of cheap sensors, sensor-equipped devices (e.g., drones), and sensor networks (such as Internet of Things), as well as the development of inexpensive human-machine interaction interfaces, the ability to quickly and effectively process sequential data is becoming more and more important. There are many tasks that may benefit from advancement in this field, ranging from monitoring and classification of human behavior to prediction of future events. Most of the above tasks require pattern recognition and machine learning capabilities. There are many approaches that have been

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Jiménez-Pérez, Guillermo. "Deep learning and unsupervised machine learning for the quantification and interpretation of electrocardiographic signals." Doctoral thesis, Universitat Pompeu Fabra, 2022. http://hdl.handle.net/10803/673555.

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Las señales electrocardiográficas, ya sea adquiridas en la piel del paciente (electrocardiogamas de superficie, ECG) o de forma invasiva mediante cateterismo (electrocardiogramas intracavitarios, iECG) ayudan a explorar la condición y función cardíacas del paciente, dada su capacidad para representar la actividad eléctrica del corazón. Sin embargo, la interpretación de las señales de ECG e iECG es una tarea difícil que requiere años de experiencia, con criterios diagnósticos complejos para personal clínico no especialista, que en muchos casos deben ser interpretados durante situaciones

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Moosavi, Seyedeh Samira. "Fingerprint-based localization in massive MIMO systems using machine learning and deep learning methods." Doctoral thesis, Université Laval, 2021. http://hdl.handle.net/20.500.11794/69511.

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À mesure que les réseaux de communication sans fil se développent vers la 5G, une énorme quantité de données sera produite et partagée sur la nouvelle plate-forme qui pourra être utilisée pour promouvoir de nouveaux services. Parmis ceux-ci, les informations de localisation des terminaux mobiles (MT) sont remarquablement utiles. Par exemple, les informations de localisation peuvent être utilisées dans différents cas de services d'enquête et d'information, de services communautaires, de suivi personnel, ainsi que de communications sensibles à la localisation. De nos jours, bien que le système d

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Mukhtar, Hind. "Machine Learning Enabled-Localization in 5G and LTE Using Image Classification and Deep Learning." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42449.

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Demand for localization has been growing due to the increase in location-based services and high bandwidth applications requiring precise localization of users to improve resource management and beam forming. Outdoor localization has been traditionally done through Global Positioning System (GPS), however it’s performance degrades in urban settings due to obstruction and multi-path effects, creating the need for better localization techniques. This thesis proposes a technique using a cascaded approach composed of image classification and deep learning using LIDAR or satellit

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Janagam, Anirudh, and Saddam Hossen. "Analysis of Network Intrusion Detection System with Machine Learning Algorithms (Deep Reinforcement Learning Algorithm)." Thesis, Blekinge Tekniska Högskola, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-17126.

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Cabrera, Dalmazzo David. "Machine learning and deep neural networks approach to modelling musical gestures." Doctoral thesis, Universitat Pompeu Fabra, 2020. http://hdl.handle.net/10803/670399.

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Gestures can be defined as a form of non-verbal communication associated with an intention or an emotional state articulation. They are not only intrinsically part of the human language, but also explain specific details of a body-knowledge execution. Gestures are being studied not only in the language research field but also in dance, sports, rehabilitation, and music; where the term is understood as a “learned technique of the body”. Therefore, in music education, gestures are assumed as automatic-motor abilities learned by repetitional practice, to self-teach and fine-tune the motor actions

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Halle, Alex, and Alexander Hasse. "Topologieoptimierung mittels Deep Learning." Technische Universität Chemnitz, 2019. https://monarch.qucosa.de/id/qucosa%3A34343.

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Die Topologieoptimierung ist die Suche einer optimalen Bauteilgeometrie in Abhängigkeit des Einsatzfalls. Für komplexe Probleme kann die Topologieoptimierung aufgrund eines hohen Detailgrades viel Zeit- und Rechenkapazität erfordern. Diese Nachteile der Topologieoptimierung sollen mittels Deep Learning reduziert werden, so dass eine Topologieoptimierung dem Konstrukteur als sekundenschnelle Hilfe dient. Das Deep Learning ist die Erweiterung künstlicher neuronaler Netzwerke, mit denen Muster oder Verhaltensregeln erlernt werden können. So soll die bislang numerisch berechnete Topologieoptimieru

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Valeriana, Riccardo. "Deep Learning: Algoritmo di Classificazione Immagini." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/17557/.

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La presente tesi si occupa della descrizione dell’algoritmo di Deep Learning che permette la classificazione di immagini importate dal dataset CIFAR-10. Il codice è stato testato in un ambiente di test per un classificatore di immagini per piattaforme Apple macOS, dove sono state utilizzate immagini di frutta e verdura come input per la classificazione. Nel primo capitolo viene trattato il dataset CIFAR-10, come questo è stato realizzato, la sua struttura e l’uso che se ne può fare. Successivamente viene descritto il metodo di apprendimento automatico della Classificazione Lineare, in che c

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Potuaud, Sylvain. "Human Grasp Synthesis with Deep Learning." Thesis, KTH, Robotik, perception och lärande, RPL, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229915.

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The human hands are one of the most complex organs of the human body. As they enable us to grasp various objects in many different ways, they have played a crucial role in the rise of the human race. Being able to control hands as a human do is a key step towards friendly human-robots interaction and realistic virtual human simulations. Grasp generation has been mostly studied for the purpose of generating physically stable grasps. This paper addresses a different aspect: how to generate realistic, natural looking grasps that are similar to human grasps. To simplify the problem, the wrist posi

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Yan, Jie Lu. "Development and validation of deep learning classifiers for antimicrobial peptide prediction." Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3881886.

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Hesamifard, Ehsan. "Privacy Preserving Machine Learning as a Service." Thesis, University of North Texas, 2020. https://digital.library.unt.edu/ark:/67531/metadc1703277/.

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Machine learning algorithms based on neural networks have achieved remarkable results and are being extensively used in different domains. However, the machine learning algorithms requires access to raw data which is often privacy sensitive. To address this issue, we develop new techniques to provide solutions for running deep neural networks over encrypted data. In this paper, we develop new techniques to adopt deep neural networks within the practical limitation of current homomorphic encryption schemes. We focus on training and classification of the well-known neural networks and convolutio

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Solenne, Andrea. "Machine Learning nell'era del Digital Marketing." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20476/.

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Il Machine Learning è una branca dell’intelligenza artificiale avente lo scopo di migliorare autonomamente l’identificazione di pattern nei dati. L'obiettivo di questo elaborato è capire se gli strumenti che mette a disposizine il machine learning possano fare la differenza nella creazione di strategie di digital marketing. Per rispondere a questa domanda sono stati presi in considerazione come casi studio, aziende che hanno utilizzato o utilizzano algoritmi di machine learning per il raggiungimento o miglioramento delle proprie strategie di marketing. Sulla base dei casi analizzati, si è dedo

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Berry, Jeffrey James. "Machine Learning Methods for Articulatory Data." Diss., The University of Arizona, 2012. http://hdl.handle.net/10150/223348.

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Humans make use of more than just the audio signal to perceive speech. Behavioral and neurological research has shown that a person's knowledge of how speech is produced influences what is perceived. With methods for collecting articulatory data becoming more ubiquitous, methods for extracting useful information are needed to make this data useful to speech scientists, and for speech technology applications. This dissertation presents feature extraction methods for ultrasound images of the tongue and for data collected with an Electro-Magnetic Articulograph (EMA). The usefulness of these featu

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Sharma, Rajesh Kumar. "Developing new algorithms for machine learning and deep learning." Thesis, 2016. http://localhost:8080/xmlui/handle/12345678/7131.

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Araújo, Miguel Diogo Ferraz. "Machine learning agents for computer games." Master's thesis, 2021. http://hdl.handle.net/10773/31371.

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In recent years, new Reinforcement Learning algorithms have been developed. These algorithms use Deep Neural Networks to represent the agent’s knowledge. After surpassing previous Artificial Intelligence (AI) milestones, such as Chess and Go, these Deep Reinforcement Learning (DRL) methods were able to surpass the human level in very complex games like Dota 2, where long-term planning is required and in which professional teams of human players train daily to win e-sports competitions. These algorithms start from scratch, do not use examples of human behavior, and can be applied in vari

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Piastka, Konrad. "Deep Semantic Learning Machine Initial design and experiments." Master's thesis, 2021. http://hdl.handle.net/10362/123465.

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Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics<br>Computer vision is an interdisciplinary scientific field that allows the digital world to interact with the real world. It is one of the fastest-growing and most important areas of data science. Applications are endless, given various tasks that can be solved thanks to the advances in the computer vision field. Examples of types of tasks that can be solved thanks to computer vision models are: image analysis, object detection, image transformation, and image generation.

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