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You searched for subject:(keras). Showing records 1 – 30 of 61 total matches.

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Brno University of Technology

1. Černohorská, Lucie. Klasifikace arteriálního a žilního řečiště v obrazových datech sítnice: Classification of arteries and veins in retinal image data.

Degree: 2020, Brno University of Technology

 This master's thesis deals with the classification of the retinal blood vessels in retinal image data. The thesis contains a description of anatomy of the… (more)

Subjects/Keywords: Sítnice; cévní řečiště sítnice; klasifikace; hluboké učení; Keras; Tensorflow; Retina; blood vessels of retina; classification; deep learning; Keras; Tensorflow

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Černohorská, L. (2020). Klasifikace arteriálního a žilního řečiště v obrazových datech sítnice: Classification of arteries and veins in retinal image data. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/189150

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Černohorská, Lucie. “Klasifikace arteriálního a žilního řečiště v obrazových datech sítnice: Classification of arteries and veins in retinal image data.” 2020. Thesis, Brno University of Technology. Accessed May 10, 2021. http://hdl.handle.net/11012/189150.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Černohorská, Lucie. “Klasifikace arteriálního a žilního řečiště v obrazových datech sítnice: Classification of arteries and veins in retinal image data.” 2020. Web. 10 May 2021.

Vancouver:

Černohorská L. Klasifikace arteriálního a žilního řečiště v obrazových datech sítnice: Classification of arteries and veins in retinal image data. [Internet] [Thesis]. Brno University of Technology; 2020. [cited 2021 May 10]. Available from: http://hdl.handle.net/11012/189150.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Černohorská L. Klasifikace arteriálního a žilního řečiště v obrazových datech sítnice: Classification of arteries and veins in retinal image data. [Thesis]. Brno University of Technology; 2020. Available from: http://hdl.handle.net/11012/189150

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Brno University of Technology

2. Čabala, Roman. Rozpoznání druhu vozidla v obraze: Recognition of Vehicle Class in Image.

Degree: 2020, Brno University of Technology

 The goal of this bachelor thesis is to recognize the type of vehicle from the image using neural networks. Vehicles are divided into 6 types,… (more)

Subjects/Keywords: klasifikácia druhu vozidla; Python; Tensorflow; Keras; VGG16; ResNet50; Xception; InceptionResNet; vehicle type classification; Python; Tensorflow; Keras; VGG16; ResNet50; Xception; InceptionResNet

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APA (6th Edition):

Čabala, R. (2020). Rozpoznání druhu vozidla v obraze: Recognition of Vehicle Class in Image. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/191677

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Čabala, Roman. “Rozpoznání druhu vozidla v obraze: Recognition of Vehicle Class in Image.” 2020. Thesis, Brno University of Technology. Accessed May 10, 2021. http://hdl.handle.net/11012/191677.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Čabala, Roman. “Rozpoznání druhu vozidla v obraze: Recognition of Vehicle Class in Image.” 2020. Web. 10 May 2021.

Vancouver:

Čabala R. Rozpoznání druhu vozidla v obraze: Recognition of Vehicle Class in Image. [Internet] [Thesis]. Brno University of Technology; 2020. [cited 2021 May 10]. Available from: http://hdl.handle.net/11012/191677.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Čabala R. Rozpoznání druhu vozidla v obraze: Recognition of Vehicle Class in Image. [Thesis]. Brno University of Technology; 2020. Available from: http://hdl.handle.net/11012/191677

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Brno University of Technology

3. Kuvik, Michal. Rozpoznávání druhu jídla s pomocí hlubokých neuronových sítí: Food classification using deep neural networks.

Degree: 2019, Brno University of Technology

 The aim of this thesis is to study problems of deep convolutional neural networks and the connected classification of images and to experiment with the… (more)

Subjects/Keywords: Python; Keras; TensorFlow; konvolučná neurónová sieť; InceptionV3; Kaggle; klasifikácia obrázkov; Python; Keras; TensorFlow; convolutional neural network; Inception; Kaggle; image classification

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Kuvik, M. (2019). Rozpoznávání druhu jídla s pomocí hlubokých neuronových sítí: Food classification using deep neural networks. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/177576

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Kuvik, Michal. “Rozpoznávání druhu jídla s pomocí hlubokých neuronových sítí: Food classification using deep neural networks.” 2019. Thesis, Brno University of Technology. Accessed May 10, 2021. http://hdl.handle.net/11012/177576.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Kuvik, Michal. “Rozpoznávání druhu jídla s pomocí hlubokých neuronových sítí: Food classification using deep neural networks.” 2019. Web. 10 May 2021.

Vancouver:

Kuvik M. Rozpoznávání druhu jídla s pomocí hlubokých neuronových sítí: Food classification using deep neural networks. [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2021 May 10]. Available from: http://hdl.handle.net/11012/177576.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Kuvik M. Rozpoznávání druhu jídla s pomocí hlubokých neuronových sítí: Food classification using deep neural networks. [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/177576

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Brno University of Technology

4. Nimrichter, Adam. Analýza finančních trhů s pomocí hlubokého učení: Financial market analysis using deep learning algorithm.

Degree: 2019, Brno University of Technology

 The thesis deals with methods for analysis of financial markets, focused on cryptocurrencies. The theoretical part, in a context of virtual currencies, describes block-chain technology,… (more)

Subjects/Keywords: Neuronová síť; virtuální měna; predikční systém; LSTM; Keras; TensorFlow.; Neural network; virtual currency; prediction system; LSTM; Keras; TensorFlow.

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APA (6th Edition):

Nimrichter, A. (2019). Analýza finančních trhů s pomocí hlubokého učení: Financial market analysis using deep learning algorithm. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/80757

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Nimrichter, Adam. “Analýza finančních trhů s pomocí hlubokého učení: Financial market analysis using deep learning algorithm.” 2019. Thesis, Brno University of Technology. Accessed May 10, 2021. http://hdl.handle.net/11012/80757.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Nimrichter, Adam. “Analýza finančních trhů s pomocí hlubokého učení: Financial market analysis using deep learning algorithm.” 2019. Web. 10 May 2021.

Vancouver:

Nimrichter A. Analýza finančních trhů s pomocí hlubokého učení: Financial market analysis using deep learning algorithm. [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2021 May 10]. Available from: http://hdl.handle.net/11012/80757.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Nimrichter A. Analýza finančních trhů s pomocí hlubokého učení: Financial market analysis using deep learning algorithm. [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/80757

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


NSYSU

5. Chen, Hung-kai. Improving Trading Strategy in Stock Pattern Recognition by Deep Learning.

Degree: Master, Finance, 2018, NSYSU

 Due to the improvement of computing power of hardware and the software of Deep Learning model, a lot of Institutional investors have been using Deep… (more)

Subjects/Keywords: Deep Learning; Keras; LVQ; Technical Analysis; Patten Recognition

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APA (6th Edition):

Chen, H. (2018). Improving Trading Strategy in Stock Pattern Recognition by Deep Learning. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0606118-153155

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Chen, Hung-kai. “Improving Trading Strategy in Stock Pattern Recognition by Deep Learning.” 2018. Thesis, NSYSU. Accessed May 10, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0606118-153155.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Chen, Hung-kai. “Improving Trading Strategy in Stock Pattern Recognition by Deep Learning.” 2018. Web. 10 May 2021.

Vancouver:

Chen H. Improving Trading Strategy in Stock Pattern Recognition by Deep Learning. [Internet] [Thesis]. NSYSU; 2018. [cited 2021 May 10]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0606118-153155.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Chen H. Improving Trading Strategy in Stock Pattern Recognition by Deep Learning. [Thesis]. NSYSU; 2018. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0606118-153155

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

6. Carlsson, Oscar. Gauge equivariant convolutional neural networks .

Degree: Chalmers tekniska högskola / Institutionen för fysik, 2020, Chalmers University of Technology

 In this thesis we present a review of the current theory of group and gauge equivariant convolutional neural networks on homogeneous spaces and general smooth… (more)

Subjects/Keywords: Convolutional neural networks; machine learning; manifolds; group; gauge; Python; Tensorflow; Keras

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APA (6th Edition):

Carlsson, O. (2020). Gauge equivariant convolutional neural networks . (Thesis). Chalmers University of Technology. Retrieved from http://hdl.handle.net/20.500.12380/301431

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Carlsson, Oscar. “Gauge equivariant convolutional neural networks .” 2020. Thesis, Chalmers University of Technology. Accessed May 10, 2021. http://hdl.handle.net/20.500.12380/301431.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Carlsson, Oscar. “Gauge equivariant convolutional neural networks .” 2020. Web. 10 May 2021.

Vancouver:

Carlsson O. Gauge equivariant convolutional neural networks . [Internet] [Thesis]. Chalmers University of Technology; 2020. [cited 2021 May 10]. Available from: http://hdl.handle.net/20.500.12380/301431.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Carlsson O. Gauge equivariant convolutional neural networks . [Thesis]. Chalmers University of Technology; 2020. Available from: http://hdl.handle.net/20.500.12380/301431

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


California State University – San Bernardino

7. Nambiar, Vipin. A General Conversational Chatbot.

Degree: MSin Computer Science, School of Computer Science and Engineering, 2021, California State University – San Bernardino

  A chatbot is software which simulates a human being in a conversation. This project involved developing a text-based general conversation chatbot. The chatbot is… (more)

Subjects/Keywords: chatbot; LSTM; NLP; tensorflow; keras; Other Computer Engineering

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APA (6th Edition):

Nambiar, V. (2021). A General Conversational Chatbot. (Thesis). California State University – San Bernardino. Retrieved from https://scholarworks.lib.csusb.edu/etd/1168

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Nambiar, Vipin. “A General Conversational Chatbot.” 2021. Thesis, California State University – San Bernardino. Accessed May 10, 2021. https://scholarworks.lib.csusb.edu/etd/1168.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Nambiar, Vipin. “A General Conversational Chatbot.” 2021. Web. 10 May 2021.

Vancouver:

Nambiar V. A General Conversational Chatbot. [Internet] [Thesis]. California State University – San Bernardino; 2021. [cited 2021 May 10]. Available from: https://scholarworks.lib.csusb.edu/etd/1168.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Nambiar V. A General Conversational Chatbot. [Thesis]. California State University – San Bernardino; 2021. Available from: https://scholarworks.lib.csusb.edu/etd/1168

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

8. Blanco Rodríguez, Jorge Esteban. Classificação de sinais de EEG para potenciais visuais evocados utilizando Deep Learning.

Degree: 2020, Universidade Estadual Paulista (UNESP)

Submitted by Jorge Esteban Blanco Rodriguez ([email protected]) on 2020-03-26T20:28:19Z No. of bitstreams: 1 blancorodriguez_je_dr_ilha.pdf: 6499581 bytes, checksum: 4206a25bcd1b140a7bd5cff5e4f681d9 (MD5)

Submitted by Jorge Esteban Blanco Rodriguez… (more)

Subjects/Keywords: CCN; EEG; Deep learning; Tensorflow; Keras; Machine learning

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APA (6th Edition):

Blanco Rodríguez, J. E. (2020). Classificação de sinais de EEG para potenciais visuais evocados utilizando Deep Learning. (Doctoral Dissertation). Universidade Estadual Paulista (UNESP). Retrieved from http://hdl.handle.net/11449/192011

Chicago Manual of Style (16th Edition):

Blanco Rodríguez, Jorge Esteban. “Classificação de sinais de EEG para potenciais visuais evocados utilizando Deep Learning.” 2020. Doctoral Dissertation, Universidade Estadual Paulista (UNESP). Accessed May 10, 2021. http://hdl.handle.net/11449/192011.

MLA Handbook (7th Edition):

Blanco Rodríguez, Jorge Esteban. “Classificação de sinais de EEG para potenciais visuais evocados utilizando Deep Learning.” 2020. Web. 10 May 2021.

Vancouver:

Blanco Rodríguez JE. Classificação de sinais de EEG para potenciais visuais evocados utilizando Deep Learning. [Internet] [Doctoral dissertation]. Universidade Estadual Paulista (UNESP); 2020. [cited 2021 May 10]. Available from: http://hdl.handle.net/11449/192011.

Council of Science Editors:

Blanco Rodríguez JE. Classificação de sinais de EEG para potenciais visuais evocados utilizando Deep Learning. [Doctoral Dissertation]. Universidade Estadual Paulista (UNESP); 2020. Available from: http://hdl.handle.net/11449/192011


Brno University of Technology

9. Hrabal, Matěj. Matení algoritmů počítačového vidění: Fooling of Algorithms of Computer Vision.

Degree: 2019, Brno University of Technology

 The goal of this work was to research existing methods of computer vision and computer recognition fooling. My focus was on group of methods called… (more)

Subjects/Keywords: počítačové vidění; CIFAR-10; diferenciální evoluce; pixel attack; Keras; TensorFlow; Python; computer vision; CIFAR-10; differential evolution; pixel attack; Keras; TensorFlow; Python

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APA (6th Edition):

Hrabal, M. (2019). Matení algoritmů počítačového vidění: Fooling of Algorithms of Computer Vision. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/180242

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Hrabal, Matěj. “Matení algoritmů počítačového vidění: Fooling of Algorithms of Computer Vision.” 2019. Thesis, Brno University of Technology. Accessed May 10, 2021. http://hdl.handle.net/11012/180242.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Hrabal, Matěj. “Matení algoritmů počítačového vidění: Fooling of Algorithms of Computer Vision.” 2019. Web. 10 May 2021.

Vancouver:

Hrabal M. Matení algoritmů počítačového vidění: Fooling of Algorithms of Computer Vision. [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2021 May 10]. Available from: http://hdl.handle.net/11012/180242.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Hrabal M. Matení algoritmů počítačového vidění: Fooling of Algorithms of Computer Vision. [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/180242

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Brno University of Technology

10. Hložek, Bohuslav. Bayesovské a neuronové sítě: Bayesian and Neural Networks.

Degree: 2019, Brno University of Technology

 This paper introduces Bayesian neural network based on Occams razor. Basic knowledge about neural networks and Bayes rule is summarized in the first part of… (more)

Subjects/Keywords: Bayesovské neuronové sítě; Umělé neuronové sítě; Occamova břitva; Neuron; Přeučení; Bayesovo pravidlo; TensorFlow; Edward; Keras; Bayesian neural networks; Artificial neural networks; Occams razor; Neuron; Overfitting; Bayes rule; TensorFlow; Edward; Keras

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APA (6th Edition):

Hložek, B. (2019). Bayesovské a neuronové sítě: Bayesian and Neural Networks. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/69530

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Hložek, Bohuslav. “Bayesovské a neuronové sítě: Bayesian and Neural Networks.” 2019. Thesis, Brno University of Technology. Accessed May 10, 2021. http://hdl.handle.net/11012/69530.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Hložek, Bohuslav. “Bayesovské a neuronové sítě: Bayesian and Neural Networks.” 2019. Web. 10 May 2021.

Vancouver:

Hložek B. Bayesovské a neuronové sítě: Bayesian and Neural Networks. [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2021 May 10]. Available from: http://hdl.handle.net/11012/69530.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Hložek B. Bayesovské a neuronové sítě: Bayesian and Neural Networks. [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/69530

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Brno University of Technology

11. Mlynarič, Tomáš. Hluboké neuronové sítě pro klasifikaci objektů v obraze: Deep Neural Networks for Classifying Objects in an Image.

Degree: 2019, Brno University of Technology

 This paper deals with classifying objects using deep neural networks. Whole scene segmentation was used as main algorithm for the classification purpose which works with… (more)

Subjects/Keywords: segmentace obrazu; monokulární kamera; hluboké neuronové sítě; video; optický tok; warping; Cityscapes; Keras; Tensorflow; image segmentation; monocular camera; deep neural networks; video; optical flow; warping; Cityscapes; Keras; Tensorflow

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APA (6th Edition):

Mlynarič, T. (2019). Hluboké neuronové sítě pro klasifikaci objektů v obraze: Deep Neural Networks for Classifying Objects in an Image. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/84863

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Mlynarič, Tomáš. “Hluboké neuronové sítě pro klasifikaci objektů v obraze: Deep Neural Networks for Classifying Objects in an Image.” 2019. Thesis, Brno University of Technology. Accessed May 10, 2021. http://hdl.handle.net/11012/84863.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Mlynarič, Tomáš. “Hluboké neuronové sítě pro klasifikaci objektů v obraze: Deep Neural Networks for Classifying Objects in an Image.” 2019. Web. 10 May 2021.

Vancouver:

Mlynarič T. Hluboké neuronové sítě pro klasifikaci objektů v obraze: Deep Neural Networks for Classifying Objects in an Image. [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2021 May 10]. Available from: http://hdl.handle.net/11012/84863.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Mlynarič T. Hluboké neuronové sítě pro klasifikaci objektů v obraze: Deep Neural Networks for Classifying Objects in an Image. [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/84863

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Brno University of Technology

12. Rozhoňová, Andrea. Metody hlubokého učení pro segmentaci cév a optického disku v oftalmologických sekvencích: Deep learning methods for vessel and optic disc segmentation in ophthalmologic sequences.

Degree: 2019, Brno University of Technology

 The aim of the following thesis was to study the issue of optical disc and retinal vessels segmentation in ophthalmologic sequences. The theoretical part of… (more)

Subjects/Keywords: cévy sítnice; hluboké učení; Keras; konvoluční neuronové sítě; MaskRCNN; oftalmologie; optický disk; segmentace obrazu; Unet; Retina vessels; Deep learning; Keras; Convolutional neural networks; MaskRCNN; Ophthalmology; Optic disc; Image segmentation; Unet

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APA (6th Edition):

Rozhoňová, A. (2019). Metody hlubokého učení pro segmentaci cév a optického disku v oftalmologických sekvencích: Deep learning methods for vessel and optic disc segmentation in ophthalmologic sequences. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/177634

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Rozhoňová, Andrea. “Metody hlubokého učení pro segmentaci cév a optického disku v oftalmologických sekvencích: Deep learning methods for vessel and optic disc segmentation in ophthalmologic sequences.” 2019. Thesis, Brno University of Technology. Accessed May 10, 2021. http://hdl.handle.net/11012/177634.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Rozhoňová, Andrea. “Metody hlubokého učení pro segmentaci cév a optického disku v oftalmologických sekvencích: Deep learning methods for vessel and optic disc segmentation in ophthalmologic sequences.” 2019. Web. 10 May 2021.

Vancouver:

Rozhoňová A. Metody hlubokého učení pro segmentaci cév a optického disku v oftalmologických sekvencích: Deep learning methods for vessel and optic disc segmentation in ophthalmologic sequences. [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2021 May 10]. Available from: http://hdl.handle.net/11012/177634.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Rozhoňová A. Metody hlubokého učení pro segmentaci cév a optického disku v oftalmologických sekvencích: Deep learning methods for vessel and optic disc segmentation in ophthalmologic sequences. [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/177634

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Brno University of Technology

13. Talár, Ondřej. Redukce šumu audionahrávek pomocí hlubokých neuronových sítí: Audio noise reduction using deep neural networks.

Degree: 2019, Brno University of Technology

 The thesis focuses on the use of deep recurrent neural network, architecture Long Short-Term Memory for robust denoising of audio signal. LSTM is currently very… (more)

Subjects/Keywords: Long Short-Term Memory (LSTM); hluboké učení; tvorba datasetu; KERAS; odšumování; rekurentní neuronová síť; Long Short-Term Memory (LSTM); deep learning; created dataset; KERAS; denoising; reccurent neural network

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Talár, O. (2019). Redukce šumu audionahrávek pomocí hlubokých neuronových sítí: Audio noise reduction using deep neural networks. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/65788

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Talár, Ondřej. “Redukce šumu audionahrávek pomocí hlubokých neuronových sítí: Audio noise reduction using deep neural networks.” 2019. Thesis, Brno University of Technology. Accessed May 10, 2021. http://hdl.handle.net/11012/65788.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Talár, Ondřej. “Redukce šumu audionahrávek pomocí hlubokých neuronových sítí: Audio noise reduction using deep neural networks.” 2019. Web. 10 May 2021.

Vancouver:

Talár O. Redukce šumu audionahrávek pomocí hlubokých neuronových sítí: Audio noise reduction using deep neural networks. [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2021 May 10]. Available from: http://hdl.handle.net/11012/65788.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Talár O. Redukce šumu audionahrávek pomocí hlubokých neuronových sítí: Audio noise reduction using deep neural networks. [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/65788

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Brno University of Technology

14. Talár, Ondřej. Redukce šumu audionahrávek pomocí hlubokých neuronových sítí: Audio noise reduction using deep neural networks.

Degree: 2019, Brno University of Technology

 The thesis focuses on the use of deep recurrent neural network, architecture Long Short-Term Memory for robust denoising of audio signal. LSTM is currently very… (more)

Subjects/Keywords: Long Short-Term Memory (LSTM); hluboké učení; tvorba datasetu; KERAS; odšumování; rekurentní neuronová síť; Long Short-Term Memory (LSTM); deep learning; created dataset; KERAS; denoising; reccurent neural network

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Talár, O. (2019). Redukce šumu audionahrávek pomocí hlubokých neuronových sítí: Audio noise reduction using deep neural networks. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/69313

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Talár, Ondřej. “Redukce šumu audionahrávek pomocí hlubokých neuronových sítí: Audio noise reduction using deep neural networks.” 2019. Thesis, Brno University of Technology. Accessed May 10, 2021. http://hdl.handle.net/11012/69313.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Talár, Ondřej. “Redukce šumu audionahrávek pomocí hlubokých neuronových sítí: Audio noise reduction using deep neural networks.” 2019. Web. 10 May 2021.

Vancouver:

Talár O. Redukce šumu audionahrávek pomocí hlubokých neuronových sítí: Audio noise reduction using deep neural networks. [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2021 May 10]. Available from: http://hdl.handle.net/11012/69313.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Talár O. Redukce šumu audionahrávek pomocí hlubokých neuronových sítí: Audio noise reduction using deep neural networks. [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/69313

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Brno University of Technology

15. Kadlec, Filip. Neuronová síť pro přenos stylu: Neural network for style transfer.

Degree: 2019, Brno University of Technology

 In this bachelor’s thesis we describe machine learning, types of artificial neural networks and internal processes of neural networks, such as feedforward data processing and… (more)

Subjects/Keywords: strojové učení; hluboké učení; umělé neuronové sítě; přenos uměleckého stylu; TensorFlow; Keras; machine learning; deep learning; artificial neural networks; artistic style transfer; TensorFlow; Keras

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Kadlec, F. (2019). Neuronová síť pro přenos stylu: Neural network for style transfer. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/179351

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Kadlec, Filip. “Neuronová síť pro přenos stylu: Neural network for style transfer.” 2019. Thesis, Brno University of Technology. Accessed May 10, 2021. http://hdl.handle.net/11012/179351.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Kadlec, Filip. “Neuronová síť pro přenos stylu: Neural network for style transfer.” 2019. Web. 10 May 2021.

Vancouver:

Kadlec F. Neuronová síť pro přenos stylu: Neural network for style transfer. [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2021 May 10]. Available from: http://hdl.handle.net/11012/179351.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Kadlec F. Neuronová síť pro přenos stylu: Neural network for style transfer. [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/179351

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Brno University of Technology

16. Daňhelová, Jana. Zpětnovazební učení pro řešení herních algoritmů: Reinforcement learning for solving game algorithms.

Degree: 2018, Brno University of Technology

 The bachelor thesis Reinforcement learning for solving game algorithms is divided into two distinct parts. The theoretical part describes and compares the fundamental methods of… (more)

Subjects/Keywords: zpětnovazební učení; Had; hluboké učení; konvoluční neuronová síť; herní algoritmus; PyGame; Keras; Python 3.5; reinforcement learning; Snake; deep learning; convolutional neural network; game algorithm; PyGame; Keras; Python 3.5

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APA (6th Edition):

Daňhelová, J. (2018). Zpětnovazební učení pro řešení herních algoritmů: Reinforcement learning for solving game algorithms. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/82371

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Daňhelová, Jana. “Zpětnovazební učení pro řešení herních algoritmů: Reinforcement learning for solving game algorithms.” 2018. Thesis, Brno University of Technology. Accessed May 10, 2021. http://hdl.handle.net/11012/82371.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Daňhelová, Jana. “Zpětnovazební učení pro řešení herních algoritmů: Reinforcement learning for solving game algorithms.” 2018. Web. 10 May 2021.

Vancouver:

Daňhelová J. Zpětnovazební učení pro řešení herních algoritmů: Reinforcement learning for solving game algorithms. [Internet] [Thesis]. Brno University of Technology; 2018. [cited 2021 May 10]. Available from: http://hdl.handle.net/11012/82371.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Daňhelová J. Zpětnovazební učení pro řešení herních algoritmů: Reinforcement learning for solving game algorithms. [Thesis]. Brno University of Technology; 2018. Available from: http://hdl.handle.net/11012/82371

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Brno University of Technology

17. Rozhoňová, Andrea. Detekce a rozpoznávaní tváře s využitím platformy Raspberry Pi: Face detection and recognition with use of Raspberry Pi.

Degree: 2018, Brno University of Technology

 The following bachelor thesis is focused on the face detection and recognition in an image. The theoretical part divides methods of detection and recognition into… (more)

Subjects/Keywords: obličej; předzpracování; obraz; metody; detekce; rozpoznání; příznaky; šablony; openCV; TensorFlow; Keras; Raspberry Pi; Pi Camera; face; preprocessing; image; methods; detection; recognition; features; templates; OpenCV; TensorFlow; Keras Raspberry Pi; Pi Camera

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Rozhoňová, A. (2018). Detekce a rozpoznávaní tváře s využitím platformy Raspberry Pi: Face detection and recognition with use of Raspberry Pi. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/68186

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Rozhoňová, Andrea. “Detekce a rozpoznávaní tváře s využitím platformy Raspberry Pi: Face detection and recognition with use of Raspberry Pi.” 2018. Thesis, Brno University of Technology. Accessed May 10, 2021. http://hdl.handle.net/11012/68186.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Rozhoňová, Andrea. “Detekce a rozpoznávaní tváře s využitím platformy Raspberry Pi: Face detection and recognition with use of Raspberry Pi.” 2018. Web. 10 May 2021.

Vancouver:

Rozhoňová A. Detekce a rozpoznávaní tváře s využitím platformy Raspberry Pi: Face detection and recognition with use of Raspberry Pi. [Internet] [Thesis]. Brno University of Technology; 2018. [cited 2021 May 10]. Available from: http://hdl.handle.net/11012/68186.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Rozhoňová A. Detekce a rozpoznávaní tváře s využitím platformy Raspberry Pi: Face detection and recognition with use of Raspberry Pi. [Thesis]. Brno University of Technology; 2018. Available from: http://hdl.handle.net/11012/68186

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Brno University of Technology

18. Serečunová, Stanislava. Segmentace cévního řečiště ve snímcích sítnice metodami hlubokého učení: Blood vessel segmentation in retinal images using deep learning approaches.

Degree: 2019, Brno University of Technology

 This diploma thesis deals with the application of deep neural networks with focus on image segmentation. The theoretical part contains a description of deep neural… (more)

Subjects/Keywords: neurón; hlboké neurónové siete; konvolúcia; segmentácia obrazu; Tensorflow; Keras; snímky očného pozadia; dátová sada; neuron; convolution; deep learning neural network; image segmentation; Tensorflow; Keras; retinal images; image dataset

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Serečunová, S. (2019). Segmentace cévního řečiště ve snímcích sítnice metodami hlubokého učení: Blood vessel segmentation in retinal images using deep learning approaches. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/81548

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Serečunová, Stanislava. “Segmentace cévního řečiště ve snímcích sítnice metodami hlubokého učení: Blood vessel segmentation in retinal images using deep learning approaches.” 2019. Thesis, Brno University of Technology. Accessed May 10, 2021. http://hdl.handle.net/11012/81548.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Serečunová, Stanislava. “Segmentace cévního řečiště ve snímcích sítnice metodami hlubokého učení: Blood vessel segmentation in retinal images using deep learning approaches.” 2019. Web. 10 May 2021.

Vancouver:

Serečunová S. Segmentace cévního řečiště ve snímcích sítnice metodami hlubokého učení: Blood vessel segmentation in retinal images using deep learning approaches. [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2021 May 10]. Available from: http://hdl.handle.net/11012/81548.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Serečunová S. Segmentace cévního řečiště ve snímcích sítnice metodami hlubokého učení: Blood vessel segmentation in retinal images using deep learning approaches. [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/81548

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Brno University of Technology

19. Kolařík, Martin. Hluboké učení pro klasifikaci textů: Deep Learning for Text Classification.

Degree: 2018, Brno University of Technology

 Thesis focuses on analysis of contemporary machine learning methods used for text classification based on emotion and testing several deep neural nework architectures. Outcome of… (more)

Subjects/Keywords: CUDA; emoce; hluboké učení; keras; klasifikace; neuronové sítě; strojové učení; theano; classification; CUDA; deep learning; emotion; keras; machine learning; neural networks; theano

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Kolařík, M. (2018). Hluboké učení pro klasifikaci textů: Deep Learning for Text Classification. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/65880

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Kolařík, Martin. “Hluboké učení pro klasifikaci textů: Deep Learning for Text Classification.” 2018. Thesis, Brno University of Technology. Accessed May 10, 2021. http://hdl.handle.net/11012/65880.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Kolařík, Martin. “Hluboké učení pro klasifikaci textů: Deep Learning for Text Classification.” 2018. Web. 10 May 2021.

Vancouver:

Kolařík M. Hluboké učení pro klasifikaci textů: Deep Learning for Text Classification. [Internet] [Thesis]. Brno University of Technology; 2018. [cited 2021 May 10]. Available from: http://hdl.handle.net/11012/65880.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Kolařík M. Hluboké učení pro klasifikaci textů: Deep Learning for Text Classification. [Thesis]. Brno University of Technology; 2018. Available from: http://hdl.handle.net/11012/65880

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Brno University of Technology

20. Myška, Vojtěch. Rekurentní neuronové sítě pro klasifikaci textů: Recurrent Neural Network for Text Classification.

Degree: 2019, Brno University of Technology

 Thesis deals with the proposal of the neural networks for classification of positive and negative texts. Development took place in the Python programming language. Design… (more)

Subjects/Keywords: neuronové sítě; klasifikace textů; hluboké učení; rekurentní neuronové sítě; Keras; TensorFlow; CUDA; Kex; neural networks; texts classification; deep learning; recurrent neural networks; Keras; TensorFlow; CUDA; Kex

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APA (6th Edition):

Myška, V. (2019). Rekurentní neuronové sítě pro klasifikaci textů: Recurrent Neural Network for Text Classification. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/80785

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Myška, Vojtěch. “Rekurentní neuronové sítě pro klasifikaci textů: Recurrent Neural Network for Text Classification.” 2019. Thesis, Brno University of Technology. Accessed May 10, 2021. http://hdl.handle.net/11012/80785.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Myška, Vojtěch. “Rekurentní neuronové sítě pro klasifikaci textů: Recurrent Neural Network for Text Classification.” 2019. Web. 10 May 2021.

Vancouver:

Myška V. Rekurentní neuronové sítě pro klasifikaci textů: Recurrent Neural Network for Text Classification. [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2021 May 10]. Available from: http://hdl.handle.net/11012/80785.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Myška V. Rekurentní neuronové sítě pro klasifikaci textů: Recurrent Neural Network for Text Classification. [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/80785

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Halmstad University

21. Mårtensson, Fredrik. Sentiment Analysis of Nordic Languages.

Degree: Information Technology, 2019, Halmstad University

This thesis explores the possibility of applying sentiment analysis to extract tonality of user reviews on the Nordic languages. Data processing is performed in… (more)

Subjects/Keywords: Neural networks; LSTM; GRU; Keras; Sentiment analysis; Nordic languages; Computer Sciences; Datavetenskap (datalogi)

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APA (6th Edition):

Mårtensson, F. (2019). Sentiment Analysis of Nordic Languages. (Thesis). Halmstad University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-39884

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Mårtensson, Fredrik. “Sentiment Analysis of Nordic Languages.” 2019. Thesis, Halmstad University. Accessed May 10, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-39884.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Mårtensson, Fredrik. “Sentiment Analysis of Nordic Languages.” 2019. Web. 10 May 2021.

Vancouver:

Mårtensson F. Sentiment Analysis of Nordic Languages. [Internet] [Thesis]. Halmstad University; 2019. [cited 2021 May 10]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-39884.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Mårtensson F. Sentiment Analysis of Nordic Languages. [Thesis]. Halmstad University; 2019. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-39884

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

22. LJUNGGREN, ELIN. Multimodal deep learning for diagnosing sub-aneurysmal aortic dilatation .

Degree: Chalmers tekniska högskola / Institutionen för data och informationsteknik, 2019, Chalmers University of Technology

 Abdominal Aortic Aneurysm (AAA) is a localized enlargement of the abdominal aorta that can progress to a rupture, which will cause an internal bleeding that… (more)

Subjects/Keywords: multimodal deep learning; abdominal aortic aneurysm (AAA); subaneurysmal aortic dilatation; VGG19; Keras; heatmaps; permutation importance

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APA (6th Edition):

LJUNGGREN, E. (2019). Multimodal deep learning for diagnosing sub-aneurysmal aortic dilatation . (Thesis). Chalmers University of Technology. Retrieved from http://hdl.handle.net/20.500.12380/300040

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

LJUNGGREN, ELIN. “Multimodal deep learning for diagnosing sub-aneurysmal aortic dilatation .” 2019. Thesis, Chalmers University of Technology. Accessed May 10, 2021. http://hdl.handle.net/20.500.12380/300040.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

LJUNGGREN, ELIN. “Multimodal deep learning for diagnosing sub-aneurysmal aortic dilatation .” 2019. Web. 10 May 2021.

Vancouver:

LJUNGGREN E. Multimodal deep learning for diagnosing sub-aneurysmal aortic dilatation . [Internet] [Thesis]. Chalmers University of Technology; 2019. [cited 2021 May 10]. Available from: http://hdl.handle.net/20.500.12380/300040.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

LJUNGGREN E. Multimodal deep learning for diagnosing sub-aneurysmal aortic dilatation . [Thesis]. Chalmers University of Technology; 2019. Available from: http://hdl.handle.net/20.500.12380/300040

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

23. Khanuja, Rashmeet Kaur. Optimizing E-Commerce Product Classification Using Transfer Learning.

Degree: MS, Computer Science, 2019, San Jose State University

  The global e-commerce market is snowballing at a rate of 23% per year. In 2017, retail e-commerce users were 1.66 billion and sales worldwide… (more)

Subjects/Keywords: convolutional neural networks; deep learning; dropout; e-commerce product categorization; keras; transfer learning; Artificial Intelligence and Robotics

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APA (6th Edition):

Khanuja, R. K. (2019). Optimizing E-Commerce Product Classification Using Transfer Learning. (Masters Thesis). San Jose State University. Retrieved from https://doi.org/10.31979/etd.egyw-ktc5 ; https://scholarworks.sjsu.edu/etd_projects/679

Chicago Manual of Style (16th Edition):

Khanuja, Rashmeet Kaur. “Optimizing E-Commerce Product Classification Using Transfer Learning.” 2019. Masters Thesis, San Jose State University. Accessed May 10, 2021. https://doi.org/10.31979/etd.egyw-ktc5 ; https://scholarworks.sjsu.edu/etd_projects/679.

MLA Handbook (7th Edition):

Khanuja, Rashmeet Kaur. “Optimizing E-Commerce Product Classification Using Transfer Learning.” 2019. Web. 10 May 2021.

Vancouver:

Khanuja RK. Optimizing E-Commerce Product Classification Using Transfer Learning. [Internet] [Masters thesis]. San Jose State University; 2019. [cited 2021 May 10]. Available from: https://doi.org/10.31979/etd.egyw-ktc5 ; https://scholarworks.sjsu.edu/etd_projects/679.

Council of Science Editors:

Khanuja RK. Optimizing E-Commerce Product Classification Using Transfer Learning. [Masters Thesis]. San Jose State University; 2019. Available from: https://doi.org/10.31979/etd.egyw-ktc5 ; https://scholarworks.sjsu.edu/etd_projects/679


Karlstad University

24. Airola, Rasmus. Image Classification, Deep Learning and Convolutional Neural Networks : A Comparative Study of Machine Learning Frameworks.

Degree: Mathematics and Computer Science, 2017, Karlstad University

  The use of machine learning and specifically neural networks is a growing trend in software development, and has grown immensely in the last couple… (more)

Subjects/Keywords: machine learning; deep learning; neural networks; convolutional neural networks; tensorflow; cntk; keras; frameworks; maskininlärning; neurala nätverk; ramverk; Computer Sciences; Datavetenskap (datalogi)

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Airola, R. (2017). Image Classification, Deep Learning and Convolutional Neural Networks : A Comparative Study of Machine Learning Frameworks. (Thesis). Karlstad University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-55129

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Airola, Rasmus. “Image Classification, Deep Learning and Convolutional Neural Networks : A Comparative Study of Machine Learning Frameworks.” 2017. Thesis, Karlstad University. Accessed May 10, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-55129.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Airola, Rasmus. “Image Classification, Deep Learning and Convolutional Neural Networks : A Comparative Study of Machine Learning Frameworks.” 2017. Web. 10 May 2021.

Vancouver:

Airola R. Image Classification, Deep Learning and Convolutional Neural Networks : A Comparative Study of Machine Learning Frameworks. [Internet] [Thesis]. Karlstad University; 2017. [cited 2021 May 10]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-55129.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Airola R. Image Classification, Deep Learning and Convolutional Neural Networks : A Comparative Study of Machine Learning Frameworks. [Thesis]. Karlstad University; 2017. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-55129

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

25. Knutsson, Magnus. A COMPARATIVE STUDY OF FFN AND CNN WITHIN IMAGE RECOGNITION : The effects of training and accuracy of different artificial neural network designs.

Degree: Informatics, 2019, University of Skövde

  Image recognition and -classification is becoming more important as the need to be able to process large amounts of images is becoming more common.… (more)

Subjects/Keywords: Machine learning; Supervised learning; FeedForward Network; Convolutional Neural Network; CIFAR-10; Keras; Activation Function; Computer Sciences; Datavetenskap (datalogi)

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Knutsson, M. (2019). A COMPARATIVE STUDY OF FFN AND CNN WITHIN IMAGE RECOGNITION : The effects of training and accuracy of different artificial neural network designs. (Thesis). University of Skövde. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-17214

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Knutsson, Magnus. “A COMPARATIVE STUDY OF FFN AND CNN WITHIN IMAGE RECOGNITION : The effects of training and accuracy of different artificial neural network designs.” 2019. Thesis, University of Skövde. Accessed May 10, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-17214.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Knutsson, Magnus. “A COMPARATIVE STUDY OF FFN AND CNN WITHIN IMAGE RECOGNITION : The effects of training and accuracy of different artificial neural network designs.” 2019. Web. 10 May 2021.

Vancouver:

Knutsson M. A COMPARATIVE STUDY OF FFN AND CNN WITHIN IMAGE RECOGNITION : The effects of training and accuracy of different artificial neural network designs. [Internet] [Thesis]. University of Skövde; 2019. [cited 2021 May 10]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-17214.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Knutsson M. A COMPARATIVE STUDY OF FFN AND CNN WITHIN IMAGE RECOGNITION : The effects of training and accuracy of different artificial neural network designs. [Thesis]. University of Skövde; 2019. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-17214

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


KTH

26. Sheikholeslami, Sina. Ablation Programming for Machine Learning.

Degree: Electrical Engineering and Computer Science (EECS), 2019, KTH

As machine learning systems are being used in an increasing number of applications from analysis of satellite sensory data and health-care analytics to smart… (more)

Subjects/Keywords: Distributed Machine Learning; Distributed Systems; Ablation Studies; Apache Spark; Keras; Hopsworks; Computer and Information Sciences; Data- och informationsvetenskap

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Sheikholeslami, S. (2019). Ablation Programming for Machine Learning. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-258413

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Sheikholeslami, Sina. “Ablation Programming for Machine Learning.” 2019. Thesis, KTH. Accessed May 10, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-258413.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Sheikholeslami, Sina. “Ablation Programming for Machine Learning.” 2019. Web. 10 May 2021.

Vancouver:

Sheikholeslami S. Ablation Programming for Machine Learning. [Internet] [Thesis]. KTH; 2019. [cited 2021 May 10]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-258413.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Sheikholeslami S. Ablation Programming for Machine Learning. [Thesis]. KTH; 2019. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-258413

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

27. Rinman, David. Detector reconstruction of γ-rays .

Degree: Chalmers tekniska högskola / Institutionen för fysik, 2020, Chalmers University of Technology

 The study of nuclear reactions through measuring emitted ᵧ-rays becomes convoluted due to complex interactions with the detector crystals, leading to cross-talk between neighbouring elements.… (more)

Subjects/Keywords: artificial neural networks; convolutional neural networks; graph neural networks; gamma ray reconstruction; addback; Crystal Ball; TensorFlow; Keras

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Rinman, D. (2020). Detector reconstruction of γ-rays . (Thesis). Chalmers University of Technology. Retrieved from http://hdl.handle.net/20.500.12380/301461

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Rinman, David. “Detector reconstruction of γ-rays .” 2020. Thesis, Chalmers University of Technology. Accessed May 10, 2021. http://hdl.handle.net/20.500.12380/301461.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Rinman, David. “Detector reconstruction of γ-rays .” 2020. Web. 10 May 2021.

Vancouver:

Rinman D. Detector reconstruction of γ-rays . [Internet] [Thesis]. Chalmers University of Technology; 2020. [cited 2021 May 10]. Available from: http://hdl.handle.net/20.500.12380/301461.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Rinman D. Detector reconstruction of γ-rays . [Thesis]. Chalmers University of Technology; 2020. Available from: http://hdl.handle.net/20.500.12380/301461

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

28. Li, Jun. Texture feature extraction and classification : a comparative study between traditional methods and deep learning.

Degree: Master of Information Science, Computer Sciences, 2020, Massey University

 Image classification has always been a core problem of computer vision. With the development of deep learning, it also provides a good solution for us… (more)

Subjects/Keywords: Local binary pattern; Haralick texture; Texture extraction; Dimensionality reduction; Support vector machines; Texture classification; Transfer learning; OpenCV; sklearn; Keras; TensorFlow

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Li, J. (2020). Texture feature extraction and classification : a comparative study between traditional methods and deep learning. (Masters Thesis). Massey University. Retrieved from http://hdl.handle.net/10179/15934

Chicago Manual of Style (16th Edition):

Li, Jun. “Texture feature extraction and classification : a comparative study between traditional methods and deep learning.” 2020. Masters Thesis, Massey University. Accessed May 10, 2021. http://hdl.handle.net/10179/15934.

MLA Handbook (7th Edition):

Li, Jun. “Texture feature extraction and classification : a comparative study between traditional methods and deep learning.” 2020. Web. 10 May 2021.

Vancouver:

Li J. Texture feature extraction and classification : a comparative study between traditional methods and deep learning. [Internet] [Masters thesis]. Massey University; 2020. [cited 2021 May 10]. Available from: http://hdl.handle.net/10179/15934.

Council of Science Editors:

Li J. Texture feature extraction and classification : a comparative study between traditional methods and deep learning. [Masters Thesis]. Massey University; 2020. Available from: http://hdl.handle.net/10179/15934


Linköping University

29. Charitos, Andreas Christopoulos. Brain disease classification using multi-channel 3D convolutional neural networks.

Degree: The Division of Statistics and Machine Learning, 2021, Linköping University

  Functional magnetic resonance imaging (fMRI) technology has been used in the investigation of human brain functionality and assist in brain disease diagnosis. While fMRI… (more)

Subjects/Keywords: Deep Learning (DL); fMRI; CNNs; Tensorflow/Keras; ASD; Medical Imaging; Probability Theory and Statistics; Sannolikhetsteori och statistik

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Charitos, A. C. (2021). Brain disease classification using multi-channel 3D convolutional neural networks. (Thesis). Linköping University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-174329

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Charitos, Andreas Christopoulos. “Brain disease classification using multi-channel 3D convolutional neural networks.” 2021. Thesis, Linköping University. Accessed May 10, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-174329.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Charitos, Andreas Christopoulos. “Brain disease classification using multi-channel 3D convolutional neural networks.” 2021. Web. 10 May 2021.

Vancouver:

Charitos AC. Brain disease classification using multi-channel 3D convolutional neural networks. [Internet] [Thesis]. Linköping University; 2021. [cited 2021 May 10]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-174329.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Charitos AC. Brain disease classification using multi-channel 3D convolutional neural networks. [Thesis]. Linköping University; 2021. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-174329

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Brno University of Technology

30. Hřebíček, Pavel. Mobilní aplikace pro rozpoznání leukokorie ze snímku lidského obličeje: Mobile App for Recognition of Leukocoria in an Image of Human Face.

Degree: 2019, Brno University of Technology

 The goal of this thesis is to design and implement a multiplatform multilingual mobile application for detecting leukocoria in an image of human face for… (more)

Subjects/Keywords: Mobilní aplikace; Eye Check; Leukokorie; iOS; Android; React Native; Dlib; OpenCV; REST; Django REST framework; Konvoluční neuronová síť; Keras; Tensorflow; Mobile application; Eye Check; Leukocoria; iOS; Android; React Native; Dlib; OpenCV; REST; Django REST framework; Convolutional neural network; Keras; Tensorflow

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Hřebíček, P. (2019). Mobilní aplikace pro rozpoznání leukokorie ze snímku lidského obličeje: Mobile App for Recognition of Leukocoria in an Image of Human Face. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/180355

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Hřebíček, Pavel. “Mobilní aplikace pro rozpoznání leukokorie ze snímku lidského obličeje: Mobile App for Recognition of Leukocoria in an Image of Human Face.” 2019. Thesis, Brno University of Technology. Accessed May 10, 2021. http://hdl.handle.net/11012/180355.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Hřebíček, Pavel. “Mobilní aplikace pro rozpoznání leukokorie ze snímku lidského obličeje: Mobile App for Recognition of Leukocoria in an Image of Human Face.” 2019. Web. 10 May 2021.

Vancouver:

Hřebíček P. Mobilní aplikace pro rozpoznání leukokorie ze snímku lidského obličeje: Mobile App for Recognition of Leukocoria in an Image of Human Face. [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2021 May 10]. Available from: http://hdl.handle.net/11012/180355.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Hřebíček P. Mobilní aplikace pro rozpoznání leukokorie ze snímku lidského obličeje: Mobile App for Recognition of Leukocoria in an Image of Human Face. [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/180355

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

[1] [2] [3]

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