Log in

Relevant bibliographies by topics / Deepl learning / Journal articles

To see the other types of publications on this topic, follow the link: Deepl learning.

Journal articles on the topic 'Deepl learning'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Deepl learning.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Chagas, Edgar Thiago De Oliveira. "Deep Learning e suas aplicações na atualidade." Revista Científica Multidisciplinar Núcleo do Conhecimento 04, no. 05 (May 8, 2019): 05–26. http://dx.doi.org/10.32749/nucleodoconhecimento.com.br/administracao/deep-learning.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Chagas, Edgar Thiago De Oliveira. "Deep Learning and its applications today." Revista Científica Multidisciplinar Núcleo do Conhecimento 04, no. 05 (May 8, 2019): 05–26. http://dx.doi.org/10.32749/nucleodoconhecimento.com.br/business-administration/deep-learning-2.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Jaiswal, Tarun, and Sushma Jaiswal. "Deep Learning in Medicine." International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (June 30, 2019): 212–17. http://dx.doi.org/10.31142/ijtsrd23641.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Zitar, Raed Abu, Ammar EL-Hassan, and Oraib AL-Sahlee. "Deep Learning Recommendation System for Course Learning Outcomes Assessment." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10-SPECIAL ISSUE (October 31, 2019): 1491–78. http://dx.doi.org/10.5373/jardcs/v11sp10/20192993.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Evseenko, Alla, and Dmitrii Romannikov. "Application of Deep Q-learning and double Deep Q-learning algorithms to the task of control an inverted pendulum." Transaction of Scientific Papers of the Novosibirsk State Technical University, no. 1-2 (August 26, 2020): 7–25. http://dx.doi.org/10.17212/2307-6879-2020-1-2-7-25.

Full text

Abstract:

Today, such a branch of science as «artificial intelligence» is booming in the world. Systems built on the basis of artificial intelligence methods have the ability to perform functions that are traditionally considered the prerogative of man. Artificial intelligence has a wide range of research areas. One such area is machine learning. This article discusses the algorithms of one of the approaches of machine learning – reinforcement learning (RL), according to which a lot of research and development has been carried out over the past seven years. Development and research on this approach is mainly carried out to solve problems in Atari 2600 games or in other similar ones. In this article, reinforcement training will be applied to one of the dynamic objects – an inverted pendulum. As a model of this object, we consider a model of an inverted pendulum on a cart taken from the Gym library, which contains many models that are used to test and analyze reinforcement learning algorithms. The article describes the implementation and study of two algorithms from this approach, Deep Q-learning and Double Deep Q-learning. As a result, training, testing and training time graphs for each algorithm are presented, on the basis of which it is concluded that it is desirable to use the Double Deep Q-learning algorithm, because the training time is approximately 2 minutes and provides the best control for the model of an inverted pendulum on a cart.

APA, Harvard, Vancouver, ISO, and other styles

6

Jaiswal, Tarun, and Sushma Jaiswal. "Deep Learning Based Pain Treatment." International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (June 30, 2019): 193–211. http://dx.doi.org/10.31142/ijtsrd23639.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Sha hao, 沙浩, 刘阳哲 Liu Yangzhe, and 张永兵 Zhang Yongbing. "基于深度学习的傅里叶叠层成像技术." Laser & Optoelectronics Progress 58, no. 18 (2021): 1811020. http://dx.doi.org/10.3788/lop202158.1811020.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Park, Ingyu, and Unjoo Lee. "Automatic, Qualitative Scoring of the Clock Drawing Test (CDT) Based on U-Net, CNN and Mobile Sensor Data." Sensors 21, no. 15 (August 3, 2021): 5239. http://dx.doi.org/10.3390/s21155239.

Full text

Abstract:

The Clock Drawing Test (CDT) is a rapid, inexpensive, and popular screening tool for cognitive functions. In spite of its qualitative capabilities in diagnosis of neurological diseases, the assessment of the CDT has depended on quantitative methods as well as manual paper based methods. Furthermore, due to the impact of the advancement of mobile smart devices imbedding several sensors and deep learning algorithms, the necessity of a standardized, qualitative, and automatic scoring system for CDT has been increased. This study presents a mobile phone application, mCDT, for the CDT and suggests a novel, automatic and qualitative scoring method using mobile sensor data and deep learning algorithms: CNN, a convolutional network, U-Net, a convolutional network for biomedical image segmentation, and the MNIST (Modified National Institute of Standards and Technology) database. To obtain DeepC, a trained model for segmenting a contour image from a hand drawn clock image, U-Net was trained with 159 CDT hand-drawn images at 128 × 128 resolution, obtained via mCDT. To construct DeepH, a trained model for segmenting the hands in a clock image, U-Net was trained with the same 159 CDT 128 × 128 resolution images. For obtaining DeepN, a trained model for classifying the digit images from a hand drawn clock image, CNN was trained with the MNIST database. Using DeepC, DeepH and DeepN with the sensor data, parameters of contour (0–3 points), numbers (0–4 points), hands (0–5 points), and the center (0–1 points) were scored for a total of 13 points. From 219 subjects, performance testing was completed with images and sensor data obtained via mCDT. For an objective performance analysis, all the images were scored and crosschecked by two clinical experts in CDT scaling. Performance test analysis derived a sensitivity, specificity, accuracy and precision for the contour parameter of 89.33, 92.68, 89.95 and 98.15%, for the hands parameter of 80.21, 95.93, 89.04 and 93.90%, for the numbers parameter of 83.87, 95.31, 87.21 and 97.74%, and for the center parameter of 98.42, 86.21, 96.80 and 97.91%, respectively. From these results, the mCDT application and its scoring system provide utility in differentiating dementia disease subtypes, being valuable in clinical practice and for studies in the field.

APA, Harvard, Vancouver, ISO, and other styles

9

Tolentino, Lean Karlo S., Ronnie O. Serfa Juan, August C. Thio-ac, Maria Abigail B. Pamahoy, Joni Rose R. Forteza, and Xavier Jet O. Garcia. "Static Sign Language Recognition Using Deep Learning." International Journal of Machine Learning and Computing 9, no. 6 (December 2019): 821–27. http://dx.doi.org/10.18178/ijmlc.2019.9.6.879.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Nizami Huseyn, Elcin. "APPLICATION OF DEEP LEARNING IN MEDICAL IMAGING." NATURE AND SCIENCE 03, no. 04 (October 27, 2020): 7–13. http://dx.doi.org/10.36719/2707-1146/04/7-13.

Full text

Abstract:

Medical imaging technology plays an important role in the detection, diagnosis and treatment of diseases. Due to the instability of human expert experience, machine learning technology is expected to assist researchers and physicians to improve the accuracy of imaging diagnosis and reduce the imbalance of medical resources. This article systematically summarizes some methods of deep learning technology, introduces the application research of deep learning technology in medical imaging, and discusses the limitations of deep learning technology in medical imaging. Key words: Artificial Intelligence, Deep Learning, Medical Imaging, big data

APA, Harvard, Vancouver, ISO, and other styles

11

Kim, T., Y. Yoon, K. Lee, K. Y. Kwahk, and N. Kim. "Application of Deep Learning in Art Therapy." International Journal of Machine Learning and Computing 11, no. 6 (November 2021): 407–12. http://dx.doi.org/10.18178/ijmlc.2021.11.6.1069.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Liu Xin, 刘鑫, 陈思溢 Chen Siyi, 陈小龙 Chen Xiaolong, and 杜鑫浩 Du Xinhao. "基于深度学习的深层次多尺度特征融合目标检测算法." Laser & Optoelectronics Progress 58, no. 12 (2021): 1210029. http://dx.doi.org/10.3788/lop202158.1210029.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Liu, Qingzhong, Zhaoxian Zhou, Sarbagya Ratna Shakya, Prathyusha Uduthalapally, Mengyu Qiao, and Andrew H. Sung. "Smartphone Sensor-Based Activity Recognition by Using Machine Learning and Deep Learning Algorithms." International Journal of Machine Learning and Computing 8, no. 2 (April 2018): 121–26. http://dx.doi.org/10.18178/ijmlc.2018.8.2.674.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

Gadri, Said. "Efficient Arabic Handwritten Character Recognition based on Machine Learning and Deep Learning Approaches." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 9–17. http://dx.doi.org/10.5373/jardcs/v12sp7/20202076.

Full text

APA, Harvard, Vancouver, ISO, and other styles

15

Poomka, Pumrapee, Nittaya Kerdprasop, and Kittisak Kerdprasop. "Machine Learning Versus Deep Learning Performances on the Sentiment Analysis of Product Reviews." International Journal of Machine Learning and Computing 11, no. 2 (March 2021): 103–9. http://dx.doi.org/10.18178/ijmlc.2021.11.2.1021.

Full text

Abstract:

At this current digital era, business platforms have been drastically shifted toward online stores on internet. With the internet-based platform, customers can order goods easily using their smart phones and get delivery at their place without going to the shopping mall. However, the drawback of this business platform is that customers do not really know about the quality of the products they ordered. Therefore, such platform service often provides the review section to let previous customers leave a review about the received product. The reviews are a good source to analyze customer's satisfaction. Business owners can assess review trend as either positive or negative based on a feedback score that customers had given, but it takes too much time for human to analyze this data. In this research, we develop computational models using machine learning techniques to classify product reviews as positive or negative based on the sentiment analysis. In our experiments, we use the book review data from amazon.com to develop the models. For a machine learning based strategy, the data had been transformed with the bag of word technique before developing models using logistic regression, naïve bayes, support vector machine, and neural network algorithms. For a deep learning strategy, the word embedding is a technique that we used to transform data before applying the long short-term memory and gated recurrent unit techniques. On comparing performance of machine learning against deep learning models, we compare results from the two methods with both the preprocessed dataset and the non-preprocessed dataset. The result is that the bag of words with neural network outperforms other techniques on both non-preprocess and preprocess datasets.

APA, Harvard, Vancouver, ISO, and other styles

16

Dingli, Alexiei, and Karl Sant Fournier. "Financial Time Series Forecasting – A Deep Learning Approach." International Journal of Machine Learning and Computing 7, no. 5 (October 2017): 118–22. http://dx.doi.org/10.18178/ijmlc.2017.7.5.632.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Firdaus, Naina, and Madhuvan Dixit. "Deep Learning Techniques, Applications and Challenges: An Assessment." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 1710–14. http://dx.doi.org/10.31142/ijtsrd14437.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Damodharan, Prof P., K. Veena, and Dr N. Suguna. "Optimized Intrusion Detection System using Deep Learning Algorithm." International Journal of Trend in Scientific Research and Development Volume-3, Issue-2 (February 28, 2019): 528–34. http://dx.doi.org/10.31142/ijtsrd21447.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Gopal, Jagadeesh. "An Approach for Facial Recognition Using Deep Learning." Journal of Advanced Research in Dynamical and Control Systems 12, SP3 (February 28, 2020): 137–43. http://dx.doi.org/10.5373/jardcs/v12sp3/20201247.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Lee, Kian Yang. "Study on Deep Learning: Applications and Research Trends." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 1603–11. http://dx.doi.org/10.5373/jardcs/v12sp7/20202264.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Alhanjour, Mohammed Ahmed. "Improved HMM by Deep Learning for Ear Classification." International Journal of Innovative Research in Computer Science & Technology 6, no. 3 (May 2018): 36–42. http://dx.doi.org/10.21276/ijircst.2018.6.3.4.

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

Meng Zhang, 孟章, 丁浩 Ding Hao, 聂守平 Nie Shouping, 马骏 Ma Jun, and 袁操今 Yuan Caojin. "深度学习在数字全息显微成像中的应用." Laser & Optoelectronics Progress 58, no. 18 (2021): 1811006. http://dx.doi.org/10.3788/lop202158.1811006.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Nizami Huseyn, Elcin. "APPLICATION OF DEEP LEARNING TECHNOLOGY IN DISEASE DIAGNOSIS." NATURE AND SCIENCE 04, no. 05 (December 28, 2020): 4–11. http://dx.doi.org/10.36719/2707-1146/05/4-11.

Full text

Abstract:

The rapid development of deep learning technology provides new methods and ideas for assisting physicians in high-precision disease diagnosis. This article reviews the principles and features of deep learning models commonly used in medical disease diagnosis, namely convolutional neural networks, deep belief networks, restricted Boltzmann machines, and recurrent neural network models. Based on several typical diseases, the application of deep learning technology in the field of disease diagnosis is introduced; finally, the future development direction is proposed based on the limitations of current deep learning technology in disease diagnosis. Keywords: Artificial Intelligence; Deep Learning; Disease Diagnosis; Neural Network

APA, Harvard, Vancouver, ISO, and other styles

24

Kibria, Md Golam, and Mehmet Sevkli. "Application of Deep Learning for Credit Card Approval: A Comparison with Two Machine Learning Techniques." International Journal of Machine Learning and Computing 11, no. 4 (August 2021): 286–90. http://dx.doi.org/10.18178/ijmlc.2021.11.4.1049.

Full text

Abstract:

The increased credit card defaulters have forced the companies to think carefully before the approval of credit applications. Credit card companies usually use their judgment to determine whether a credit card should be issued to the customer satisfying certain criteria. Some machine learning algorithms have also been used to support the decision. The main objective of this paper is to build a deep learning model based on the UCI (University of California, Irvine) data sets, which can support the credit card approval decision. Secondly, the performance of the built model is compared with the other two traditional machine learning algorithms: logistic regression (LR) and support vector machine (SVM). Our results show that the overall performance of our deep learning model is slightly better than that of the other two models.

APA, Harvard, Vancouver, ISO, and other styles

25

Ansari, Hashim Shakil, and Goutam R. "Autonomous Driving using Deep Reinforcement Learning in Urban Environment." International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (April 30, 2019): 1573–75. http://dx.doi.org/10.31142/ijtsrd23442.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

Jimmington, Anjana. "A Baseline Based Deep Learning Approach of Live Tweets." International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (June 30, 2019): 829–33. http://dx.doi.org/10.31142/ijtsrd23918.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Salido, Julie Ann A., and Conrado Ruiz Jr. "Using Deep Learning for Melanoma Detection in Dermoscopy Images." International Journal of Machine Learning and Computing 8, no. 1 (February 2018): 61–68. http://dx.doi.org/10.18178/ijmlc.2018.8.1.664.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Cárdenas, Rolando J., Cesar A. Beltrán, and Juan C. Gutiérrez. "Small Face Detection Using Deep Learning on Surveillance Videos." International Journal of Machine Learning and Computing 9, no. 2 (April 2019): 189–94. http://dx.doi.org/10.18178/ijmlc.2019.9.2.785.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Thinsungnoen, Tippaya, Kittisak Kerdprasop, and Nittaya Kerdprasop. "A Deep Learning of Time Series for Efficient Analysis." International Journal of Future Computer and Communication 6, no. 3 (September 2017): 123–27. http://dx.doi.org/10.18178/ijfcc.2017.6.3.503.

Full text

APA, Harvard, Vancouver, ISO, and other styles

30

Chaddha, Mahima, Sneha Kashid, and Snehal Bhosale Prof Radha Deoghare. "Deep Learning for X-ray Image to Text Generation." International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (April 30, 2019): 1679–82. http://dx.doi.org/10.31142/ijtsrd23168.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Shen, Hua, and Jinming Gao. "Deep learning virtual colorful lens-free on-chip microscopy." Chinese Optics Letters 18, no. 12 (2020): 121705. http://dx.doi.org/10.3788/col202018.121705.

Full text

APA, Harvard, Vancouver, ISO, and other styles

32

Li Haoyu, 李浩宇, 曲丽颖 Qu Liying, 华子杰 Hua Zijie, 王新伟 Wang Xinwei, 赵唯淞 Zhao Weisong, and 刘俭 Liu Jian. "基于深度学习的荧光显微成像技术及应用." Laser & Optoelectronics Progress 58, no. 18 (2021): 1811007. http://dx.doi.org/10.3788/lop202158.1811007.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Kim, Sung Kyu. "Correlationship of Deep Learning in Big Data and pratityasamutpada." Korea Journal of Buddhist Professors 26, no. 1 (April 30, 2020): 47–64. http://dx.doi.org/10.34281/kabp.26.1.3.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

G, Ranganathan. "A Study to Find Facts Behind Preprocessing on Deep Learning Algorithms." Journal of Innovative Image Processing 3, no. 1 (April 27, 2021): 66–74. http://dx.doi.org/10.36548/jiip.2021.1.006.

Full text

Abstract:

In the near future, deep learning algorithms will be incorporated in several applications for assisting the human beings. The deep learning algorithms have the tendency to allow a computer to work on its assumption. Most of the deep learning algorithms mimic the human brain’s neuron connection to leverage an artificial intelligence to the computer system. This helps to improve the operational speed and accuracy on several critical tasks. This paper projects the blocks, which are required for the incorporation of deep learning based algorithm. Also, the paper attempts to deeply analyze the necessity of the preprocessing step over several deep learning based applications.

APA, Harvard, Vancouver, ISO, and other styles

35

Haris, Hashi, and Misha Ravi. "Phrase Structure Identification and Classification of Sentences using Deep Learning." International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (June 30, 2019): 578–81. http://dx.doi.org/10.31142/ijtsrd23841.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Banzi, Jamal, Isack Bulugu, and Zhongfu Ye. "Deep Predictive Neural Network: Unsupervised Learning for Hand Pose Estimation." International Journal of Machine Learning and Computing 9, no. 4 (August 2019): 432–39. http://dx.doi.org/10.18178/ijmlc.2019.9.4.822.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

Vo, Anh H., Van-Huy Pham, and Bao T. Nguyen. "Deep Learning for Vietnamese Sign Language Recognition in Video Sequence." International Journal of Machine Learning and Computing 9, no. 4 (August 2019): 440–45. http://dx.doi.org/10.18178/ijmlc.2019.9.4.823.

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

Shahbazi, Zeinab, and Yung-Cheol Byun. "Deep Learning Method to Estimate the Focus Time of Paragraph." International Journal of Machine Learning and Computing 10, no. 1 (January 2020): 75–80. http://dx.doi.org/10.18178/ijmlc.2020.10.1.901.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Banday, Mehroush. "Deep learning and Big Data Analysis: Challenges, Opportunities and Applications." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 2216–22. http://dx.doi.org/10.31142/ijtsrd12710.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

Sarmiento-Ramos, José Luis. "Aplicaciones de las redes neuronales y el deep learning a la ingeniería biomédica." Revista UIS Ingenierías 19, no. 4 (May 30, 2020): 1–18. http://dx.doi.org/10.18273/revuin.v19n4-2020001.

Full text

Abstract:

Hoy en día, las redes neuronales artificiales y el deep learning, son dos de las herramientas más poderosas del aprendizaje de máquina, que tienen por objetivo desarrollar sistemas que aprenden automáticamente, reconocen patrones, predicen comportamientos y generalizan información a partir de conjuntos de datos. Estasdos herramientas se han convertido en un potencial campo de investigación con aplicaciones a la ingeniería, no siendo la ingeniería biomédica la excepción. En este artículo se presenta una revisión actualizada de las principales aplicaciones de las redes neuronales y el deep learning a la ingeniería biomédica en las ramas de la ómica, la imagenología, las interfaces cerebro-máquina y hombre-máquina, y la gestión y administración de la salud pública; ramas que se extienden desde el estudio de procesos a nivel molecular, hasta procesos que involucran grandes poblaciones.Palabras clave:aprendizaje de máquina; inteligencia artificial; reconocimiento de patrones; ómica; bioinformática; biomedicina; imagenología; interfaces cerebro-máquina; interfaces hombre-máquina;salud pública.

APA, Harvard, Vancouver, ISO, and other styles

41

N., Dr Krishnaraj. "Improved Distributed Frameworks to Incorporate Big Data through Deep Learning." Journal of Advanced Research in Dynamical and Control Systems 51, SP3 (February 28, 2020): 332–38. http://dx.doi.org/10.5373/jardcs/v12sp3/20201269.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

T., Senthil Kumar. "Systematic Study on Deep Learning Techniques for Prediction of Movies." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (March 31, 2020): 31–38. http://dx.doi.org/10.5373/jardcs/v12sp4/20201463.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

R.S., Dr Sabeenian. "Efficient Gold Tree Child Items Classification System Using Deep Learning." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (March 31, 2020): 1845–59. http://dx.doi.org/10.5373/jardcs/v12sp4/20201671.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Bae, Hyun Soo, Ho Jin Lee, and Suk Gyu Lee. "Voice Recognition-Based on Adaptive MFCC and Deep Learning for Embedded Systems." Journal of Institute of Control, Robotics and Systems 22, no. 10 (October 31, 2016): 797–802. http://dx.doi.org/10.5302/j.icros.2016.16.0136.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Sen, Bandita, and V. Vedanarayanan. "Efficient Classification of Breast Lesion based on Deep Learning Technique." Bonfring International Journal of Advances in Image Processing 6, no. 1 (February 29, 2016): 01–06. http://dx.doi.org/10.9756/bijaip.10446.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

KRUPPAI, Gábor, Péter LEHOTAY-KÉRY, and Attila KISS. "BUILDING, VISUALIZING AND EXECUTING DEEP LEARNING MODELS AS DATAFLOW GRAPHS." Acta Electrotechnica et Informatica 20, no. 2 (June 30, 2020): 27–34. http://dx.doi.org/10.15546/aeei-2020-0010.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

FUNABASHI, Satoshi, Takashi SATO, Alexander SCHMITZ, and Shigeki SUGANO. "Feature Extraction by Deep Learning for Improved In-Hand Manipulation." Abstracts of the international conference on advanced mechatronics : toward evolutionary fusion of IT and mechatronics : ICAM 2015.6 (2015): 31–32. http://dx.doi.org/10.1299/jsmeicam.2015.6.31.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Zhang Caizhen, 张彩珍, 李颖 Li Ying, 康斌龙 Kang binlong, and 常元 Chang yuan. "基于深度学习的模糊车牌字符识别算法." Laser & Optoelectronics Progress 58, no. 16 (2021): 1610012. http://dx.doi.org/10.3788/lop202158.1610012.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Zhu Yuzheng, 朱育正, 张亚萍 Zhang Yaping, and 冯乔生 Feng Qiaosheng. "基于深度学习的单视图彩色三维重建." Laser & Optoelectronics Progress 58, no. 14 (2021): 1410010. http://dx.doi.org/10.3788/lop202158.1410010.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Wen Pei, 文沛, 程英蕾 Cheng Yinglei, and 余旺盛 Yu Wangsheng. "基于深度学习的点云分类方法综述." Laser & Optoelectronics Progress 58, no. 16 (2021): 1600003. http://dx.doi.org/10.3788/lop202158.1600003.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!