Relevant bibliographies by topics / Deep machine learning

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Deep machine learning'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Deep machine 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.

Journal articles on the topic "Deep machine learning"

1

Akgül, İsmail, and Yıldız Aydın. "OBJECT RECOGNITION WITH DEEP LEARNING AND MACHINE LEARNING METHODS." NWSA Academic Journals 17, no. 4 (2022): 54–61. http://dx.doi.org/10.12739/nwsa.2022.17.4.2a0189.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Jain, Migul. "Machine Learning and Deep Learning Approaches for Cybersecurity: A Review." International Journal of Science and Research (IJSR) 12, no. 10 (2023): 1706–10. http://dx.doi.org/10.21275/sr231023115126.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Rebecca, Dr B., Bathul Spandana, and Bingi Swathi. "Facial Emotion Detection using Machine Learning and Deep Learning Algorithms." International Journal of Research Publication and Reviews 6, no. 4 (2025): 14604–8. https://doi.org/10.55248/gengpi.6.0425.1663.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Shivareddy, Nareddy, and Dr V. Uma Rani. "Enhancing Image Forgery Detection Using Machine Learning And Deep Learning." International Journal of Research Publication and Reviews 6, no. 6 (2025): 12129–33. https://doi.org/10.55248/gengpi.6.0625.2390.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

P, Jayapal. "Efficient Human-Machine Interface through Deep Learning Fusion." International Journal of Science and Research (IJSR) 13, no. 1 (2024): 680–86. http://dx.doi.org/10.21275/sr24109210845.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Madhavappa Bachala Sathyanarayana, T. "A Review on Fraud Detection Using Machine Learning and Deep Learning." International Journal of Science and Research (IJSR) 13, no. 2 (2024): 438–43. http://dx.doi.org/10.21275/sr24114141555.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Fernandes, Carlos Ropelatto. "Machine Learning, Deep Learning e Aplicações." Monumenta - Revista Científica Multidisciplinar 9, no. 9 (2024): 1–2. https://doi.org/10.57077/monumenta.v9i9.261.

Full text
Abstract:
Neste minicurso será apresentado e comentado brevemente sobre alguns conceitos básicos de Aprendizagem de Máquina (Machine Learning) relacionados aos tipos de aprendizagem que elas desenvolvem as quais podem ser: Aprendizagem Supervisionada e Aprendizagem Não Supervisionada. Dentro da Aprendizagem Supervisionada encontramos os seguintes tipos de Redes Neurais: Artificiais, Convolucionais e Recorrentes. Já em Aprendizagem Não Supervisionada temos: os Mapas Auto Organizáveis, Boltz Machines, Autoencoders e Redes Adversárias Generativas. Aprendizagem Supervisionada temos algumas aplicações como c
APA, Harvard, Vancouver, ISO, and other styles
8

J, Jayashree. "Protecting the Internet of Things (IOT) with Machine Learning and Deep Learning Techniques." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 12 (2023): 1–9. http://dx.doi.org/10.55041/ijsrem27782.

Full text
Abstract:
Abstract- Deep learning (DL) and Machine learning (ML) as an IoT paradigm have improved problem-solving, and as a result, their application has expanded to many different fields. This has led to the idea that there are two powerful ways to use data—deep learning (DL) and machine learning (ML)—to solve specific problems. Thus, this article's objective is to provide a thorough analysis of "Scanning Machines and Deep Learning Techniques for Internet of Things (IOT) Security and Privacy," which addresses the current state of IoT research as well as its joint endeavor with DL. This technique stops
APA, Harvard, Vancouver, ISO, and other styles
9

Arya, Anil, A. Ashiq, M. S. Aswathy, and P. S. Akhila. "A Comparative Review of Different Techniques for Handwriting to Text Conversion." Advanced Innovations in Computer Programming Languages 7, no. 1 (2024): 1–9. https://doi.org/10.5281/zenodo.13766826.

Full text
Abstract:
<em>Handwriting to text conversion, also known as handwriting recognition, is the process of converting handwritten text into machine-readable text. This article presents a comparative review of the different machine learning techniques for handwriting to text conversion. It highlights the works of many researchers and provides an analysis of the various machine-learning techniques that are used for the handwriting to text conversion<strong>.</strong></em>
APA, Harvard, Vancouver, ISO, and other styles
10

Gaurav, Singh, Kumar Shubham, Vijayan Surya, Perumal Thinagaran, and Sathiyanarayanan Mithileysh. "CYBER BULLYING DETECTION USING MACHINE LEARNING AND DEEP LEARNING." International Journal For Technological Research In Engineering 9, no. 7 (2022): 11–17. https://doi.org/10.5281/zenodo.6392440.

Full text
Abstract:
The use of information and technology to bully a person online is referred to as cyberbullying. Individuals use Information and Communication Technology (ICT) to ridicule, embarrass, taunt, defame, intimidate, and criticise a person without making a direct eye contact. With the rise of social media, bullies have created a &ldquo;virtual playground&rdquo; in Facebook, Instagram, WhatsApp, Twitter and YouTube by targeting specific set of individuals or groups. It is necessary to deploy models and mechanisms in place for bullying contents, where the content is automatically detected and resolved,
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Deep machine learning"

1

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

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

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

Full text
Abstract:
We introduce recurrent collective classification (RCC), a variant of ICA analogous to recurrent neural network prediction. RCC accommodates any differentiable local classifier and relational feature functions. We provide gradient-based strategies for optimizing over model parameters to more directly minimize the loss function. In our experiments, this direct loss minimization translates to improved accuracy and robustness on real network data. We demonstrate the robustness of RCC in settings where local classification is very noisy, settings that are particularly challenging for ICA. As a new
APA, Harvard, Vancouver, ISO, and other styles
3

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

Full text
Abstract:
Recent research in feature learning has been extended to sequence data, where each instance consists of a sequence of heterogeneous items with a variable length. However, in many real-world applications, the data exists in the form of attributed sequences, which is composed of a set of fixed-size attributes and variable-length sequences with dependencies between them. In the attributed sequence context, feature learning remains challenging due to the dependencies between sequences and their associated attributes. In this dissertation, we focus on analyzing and building deep learning models for
APA, Harvard, Vancouver, ISO, and other styles
4

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

Full text
Abstract:
Il grande successo che il Deep Learning ha ottenuto in ambiti strategici per la nostra società quali l'industria, la difesa, la medicina etc., ha portanto sempre più realtà a investire ed esplorare l'utilizzo di questa tecnologia. Ormai si possono trovare algoritmi di Machine Learning e Deep Learning quasi in ogni ambito della nostra vita. Dai telefoni, agli elettrodomestici intelligenti fino ai veicoli che guidiamo. Quindi si può dire che questa tecnologia pervarsiva è ormai a contatto con le nostre vite e quindi dobbiamo confrontarci con essa. Da questo nasce l’eXplainable Artificial Intelli
APA, Harvard, Vancouver, ISO, and other styles
5

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

Full text
Abstract:
Cette thèse étudie les capacités des méthodes d'apprentissage automatique à raisonner sur des relations spatiales, en particulier sur les relations directionnelles, et l'utilisation de connaissances relationnelles, connues a priori, par ces méthodes. Il existe de nombreux travaux dans le domaine de l'exploitation de connaissances sur les relations dans des méthodes d'apprentissage automatique. Cependant, ce corpus de travaux laisse encore plusieurs questions ouvertes. Tout au long de cette thèse, nous explorons, étudions et tentons d'expliquer différentes questions de recherche liées à ces que
APA, Harvard, Vancouver, ISO, and other styles
6

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

Full text
Abstract:
The last decade has witnessed a tremendous increase in the amount of available biological data. Different technologies for measuring the genome, epigenome, transcriptome, proteome, metabolome, and microbiome in different organisms are producing large amounts of high-dimensional data every day. High-dimensional data provides unprecedented challenges and opportunities to gain a better understanding of biological systems. Unlike other data types, biological data imposes more constraints on researchers. Biologists are not only interested in accurate predictive models that capture complex input-out
APA, Harvard, Vancouver, ISO, and other styles
7

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

Full text
Abstract:
Since 2006, deep learning algorithms which rely on deep architectures with several layers of increasingly complex representations have been able to outperform state-of-the-art methods in several settings. Deep architectures can be very efficient in terms of the number of parameters required to represent complex operations which makes them very appealing to achieve good generalization with small amounts of data. Although training deep architectures has traditionally been considered a difficult problem, a successful approach has been to employ an unsupervised layer-wise pre-training step to init
APA, Harvard, Vancouver, ISO, and other styles
8

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

Full text
Abstract:
This thesis is concerned with the novel use of machine learning (ML) methods in soil science research. ML adoption in soil science has increased considerably, especially in pedometrics (the use of quantitative methods to study the variation of soils). In parallel, the size of the soil datasets has also increased thanks to projects of global impact that aim to rescue legacy data or new large extent surveys to collect new information. While we have big datasets and global projects, currently, modelling is mostly based on "traditional" ML approaches which do not take full advantage of these large
APA, Harvard, Vancouver, ISO, and other styles
9

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

Full text
Abstract:
With the increasing amount of data and the growing computing power, deep learning techniques using deep neural networks (DNNs) have been successfully applied in many practical artificial intelligence applications. The mini-batch stochastic gradient descent (SGD) algorithm and its variants are the most widely used algorithms in training deep models. The SGD algorithm is an iterative algorithm that needs to update the model parameters many times by traversing the training data, which is very time-consuming even using the single powerful GPU or TPU. Therefore, it becomes a common practice to expl
APA, Harvard, Vancouver, ISO, and other styles
10

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

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

Books on the topic "Deep machine learning"

1

Hu, Fei, and Xiali Hei. AI, Machine Learning and Deep Learning. CRC Press, 2023. http://dx.doi.org/10.1201/9781003187158.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Suriyan, Kannadhasan, Prasanna Devi Sivakumar, Paavai Gopalan Anand, and Durgadevi Palani, eds. Machine Learning, Deep Learning, and Blockchain. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-88237-1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Rivera, Gilberto, Alejandro Rosete, Bernabé Dorronsoro, and Nelson Rangel-Valdez, eds. Innovations in Machine and Deep Learning. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-40688-1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Tsihrintzis, George A., Maria Virvou, and Lakhmi C. Jain, eds. Advances in Machine Learning/Deep Learning-based Technologies. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-76794-5.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Hong, Huixiao, ed. Machine Learning and Deep Learning in Computational Toxicology. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-20730-3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Stamp, Mark, and Martin Jureček, eds. Machine Learning, Deep Learning and AI for Cybersecurity. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-83157-7.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Devi, K. Gayathri, Kishore Balasubramanian, and Le Anh Ngoc. Machine Learning and Deep Learning Techniques for Medical Science. CRC Press, 2022. http://dx.doi.org/10.1201/9781003217497.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Abualigah, Laith, ed. Classification Applications with Deep Learning and Machine Learning Technologies. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-17576-3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Borhani, Reza, Soheila Borhani, and Aggelos K. Katsaggelos. Fundamentals of Machine Learning and Deep Learning in Medicine. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-19502-0.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Pillai, Anitha S., and Bindu Menon. Machine Learning and Deep Learning in Neuroimaging Data Analysis. CRC Press, 2024. http://dx.doi.org/10.1201/9781003264767.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Deep machine learning"

1

Kim, Phil. "Machine Learning." In MATLAB Deep Learning. Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2845-6_1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Vasudevan, Shriram K., Sini Raj Pulari, and Subashri Vasudevan. "Machine Learning: The Fundamentals." In Deep Learning. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003185635-3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Geetha, T. V., and S. Sendhilkumar. "Other Models of Deep Learning and Applications of Deep Learning." In Machine Learning. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003290100-16.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Vermeulen, Andreas François. "Unsupervised Learning: Deep Learning." In Industrial Machine Learning. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5316-8_8.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Nath, Vishnu, and Stephen E. Levinson. "Machine Learning." In Autonomous Robotics and Deep Learning. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05603-6_6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Şen, Zekâi. "Machine Learning." In Shallow and Deep Learning Principles. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-29555-3_8.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Jo, Taeho. "Restricted Boltzmann Machine." In Deep Learning Foundations. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-32879-4_11.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Norris, Donald J. "Machine Learning: Deep Learning." In Beginning Artificial Intelligence with the Raspberry Pi. Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2743-5_8.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Joshi, Ameet V. "Deep Learning." In Machine Learning and Artificial Intelligence. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26622-6_12.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Joshi, Ameet V. "Deep Learning." In Machine Learning and Artificial Intelligence. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12282-8_13.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Deep machine learning"

1

Nasrin, Shamima, Md Zahangir Alom, Simon Khan, and Tarek M. Taha. "Deep learning-based explainable approaches for RNA-seq gene expression data analysis." In Applications of Machine Learning 2024, edited by Barath Narayanan, Michael E. Zelinski, Tarek M. Taha, Abdul A. Awwal, and Khan M. Iftekharuddin. SPIE, 2024. http://dx.doi.org/10.1117/12.3030851.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

M, Muthulakshmi, Harsha Vardhan A, Veda Sampreetha M, Hanuma Siva Sairam A, Syfullah Sd, and Sriram K. "Deep Learning Based Thyroid Tumor Prediction." In 2024 Intelligent Systems and Machine Learning Conference (ISML). IEEE, 2024. https://doi.org/10.1109/isml60050.2024.11007448.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

"DEEP-ML 2019 Program Committee." In 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML). IEEE, 2019. http://dx.doi.org/10.1109/deep-ml.2019.00007.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

"DEEP-ML 2019 Organizing Committee." In 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML). IEEE, 2019. http://dx.doi.org/10.1109/deep-ml.2019.00006.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

DeGuchy, Omar, Alex Ho, and Roummel F. Marcia. "Image disambiguation with deep neural networks." In Applications of Machine Learning, edited by Michael E. Zelinski, Tarek M. Taha, Jonathan Howe, Abdul A. Awwal, and Khan M. Iftekharuddin. SPIE, 2019. http://dx.doi.org/10.1117/12.2530230.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Padilla, Willie J. "Deep Learning the Future of Metamaterials." In Machine Learning in Photonics, edited by Francesco Ferranti, Mehdi K. Hedayati, and Andrea Fratalocchi. SPIE, 2024. http://dx.doi.org/10.1117/12.3016504.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

"Keynote Abstracts." In 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML). IEEE, 2019. http://dx.doi.org/10.1109/deep-ml.2019.00008.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

"[Title page i]." In 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML). IEEE, 2019. http://dx.doi.org/10.1109/deep-ml.2019.00001.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

"[Title page iii]." In 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML). IEEE, 2019. http://dx.doi.org/10.1109/deep-ml.2019.00002.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

"[Copyright notice]." In 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML). IEEE, 2019. http://dx.doi.org/10.1109/deep-ml.2019.00003.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Deep machine learning"

1

Vilalta, Ricardo. Modern Machine Learning Techniques. Instats Inc., 2024. http://dx.doi.org/10.61700/6sziq6usb3koe786.

Full text
Abstract:
This workshop offers a comprehensively introduction to modern machine learning techniques in Python. Designed for PhD students, professors, and professional researchers, the seminar covers a variety of valuable techniques for machine learning, from meta-learning and transfer learning, to domain adaptation, active learning, deep learning, and Bayesian networks, equipping participants with key practical skills to enhance their research capabilities.
APA, Harvard, Vancouver, ISO, and other styles
2

Fessel, Kimberly. Machine Learning in Python. Instats Inc., 2024. http://dx.doi.org/10.61700/s74zy0ivgwioe1764.

Full text
Abstract:
This intensive, hands-on workshop offers a deep dive into machine learning with Python, designed for PhD students, professors, and researchers across various fields. Participants will master practical skills in data cleaning, exploratory data analysis, and building powerful machine learning models, including neural networks, to elevate their research. With real-world coding exercises and expert guidance, this workshop will equip you with the tools to turn data into actionable insights.
APA, Harvard, Vancouver, ISO, and other styles
3

Flaxman, Seth. Statistical Machine Learning for Researchers. Instats Inc., 2023. http://dx.doi.org/10.61700/3sz8pzpbpsg2i469.

Full text
Abstract:
This workshop is designed to empower researchers with the fundamentals of machine learning using R. Participants will learn the key principles that make machine learning so effective, powering the modern AI and deep learning revolution. Through hands-on exercises, participants will gain experience applying a variety of flexible and scalable statistical machine learning methods to analyze datasets and build effective predictive models. An official Instats certificate of completion is provided along with 2 ECTS Equivalent points.
APA, Harvard, Vancouver, ISO, and other styles
4

Flaxman, Seth. Statistical Machine Learning for Researchers. Instats Inc., 2023. http://dx.doi.org/10.61700/wu1mihoap95h0469.

Full text
Abstract:
This workshop is designed to empower researchers with the fundamentals of machine learning using R. Participants will learn the key principles that make machine learning so effective, powering the modern AI and deep learning revolution. Through hands-on exercises, participants will gain experience applying a variety of flexible and scalable statistical machine learning methods to analyze datasets and build effective predictive models. An official Instats certificate of completion is provided along with 2 ECTS Equivalent points.
APA, Harvard, Vancouver, ISO, and other styles
5

Ogunbire, Abimbola, Panick Kalambay, Hardik Gajera, and Srinivas Pulugurtha. Deep Learning, Machine Learning, or Statistical Models for Weather-related Crash Severity Prediction. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2320.

Full text
Abstract:
Nearly 5,000 people are killed and more than 418,000 are injured in weather-related traffic incidents each year. Assessments of the effectiveness of statistical models applied to crash severity prediction compared to machine learning (ML) and deep learning techniques (DL) help researchers and practitioners know what models are most effective under specific conditions. Given the class imbalance in crash data, the synthetic minority over-sampling technique for nominal (SMOTE-N) data was employed to generate synthetic samples for the minority class. The ordered logit model (OLM) and the ordered p
APA, Harvard, Vancouver, ISO, and other styles
6

Varastehpour, Soheil, Hamid Sharifzadeh, and Iman Ardekani. A Comprehensive Review of Deep Learning Algorithms. Unitec ePress, 2021. http://dx.doi.org/10.34074/ocds.092.

Full text
Abstract:
Deep learning algorithms are a subset of machine learning algorithms that aim to explore several levels of the distributed representations from the input data. Recently, many deep learning algorithms have been proposed to solve traditional artificial intelligence problems. In this review paper, some of the up-to-date algorithms of this topic in the field of computer vision and image processing are reviewed. Following this, a brief overview of several different deep learning methods and their recent developments are discussed.
APA, Harvard, Vancouver, ISO, and other styles
7

Gastelum, Zoe, Laura Matzen, Mallory Stites, et al. Assessing Cognitive Impacts of Errors from Machine Learning and Deep Learning Models: Final Report. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1821527.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Ulissi, Zachary. Predicting Catalyst Surface Stability Under Reaction Conditions Using Deep Reinforcement Learning and Machine Learning Potentials. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/2324766.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Pasupuleti, Murali Krishna. Quantum-Enhanced Machine Learning: Harnessing Quantum Computing for Next-Generation AI Systems. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv125.

Full text
Abstract:
Abstract Quantum-enhanced machine learning (QML) represents a paradigm shift in artificial intelligence by integrating quantum computing principles to solve complex computational problems more efficiently than classical methods. By leveraging quantum superposition, entanglement, and parallelism, QML has the potential to accelerate deep learning training, optimize combinatorial problems, and enhance feature selection in high-dimensional spaces. This research explores foundational quantum computing concepts relevant to AI, including quantum circuits, variational quantum algorithms, and quantum k
APA, Harvard, Vancouver, ISO, and other styles
10

Bruckner, Daniel. ML-o-Scope: A Diagnostic Visualization System for Deep Machine Learning Pipelines. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada605112.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Deep machine learning' for particular source types:
Journal articles Dissertations / Theses Books
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!

To the bibliography