Feature And Dimensionality Reduction For Clustering With Deep Learning
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Feature And Dimensionality Reduction For Clustering With Deep Learning
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Author : Frederic Ros
language : en
Publisher: Springer Nature
Release Date : 2023-12-21
Feature And Dimensionality Reduction For Clustering With Deep Learning written by Frederic Ros and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-21 with Technology & Engineering categories.
This book presents an overview of recent methods of feature selection and dimensionality reduction that are based on Deep Neural Networks (DNNs) for a clustering perspective, with particular attention to the knowledge discovery question. The authors first present a synthesis of the major recent influencing techniques and "tricks" participating in recent advances in deep clustering, as well as a recall of the main deep learning architectures. Secondly, the book highlights the most popular works by “family” to provide a more suitable starting point from which to develop a full understanding of the domain. Overall, the book proposes a comprehensive up-to-date review of deep feature selection and deep clustering methods with particular attention to the knowledge discovery question and under a multi-criteria analysis. The book can be very helpful for young researchers, non-experts, and R&D AI engineers.
Data Driven Science And Engineering
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Author : Steven L. Brunton
language : en
Publisher: Cambridge University Press
Release Date : 2022-05-05
Data Driven Science And Engineering written by Steven L. Brunton and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-05 with Computers categories.
A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
Deep Learning For Data Mining Unsupervised Feature Learning And Representation
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Author : Mr. Srinivas Rao Adabala
language : en
Publisher: Xoffencerpublication
Release Date : 2023-08-14
Deep Learning For Data Mining Unsupervised Feature Learning And Representation written by Mr. Srinivas Rao Adabala and has been published by Xoffencerpublication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-14 with Computers categories.
Deep learning has developed as a useful approach for data mining tasks such as unsupervised feature learning and representation. This is thanks to its ability to learn from examples with no prior guidance. Unsupervised learning is the process of discovering patterns and structures in unlabeled data without the use of any explicit labels or annotations. This type of learning does not require the data to be annotated or labelled. This is especially helpful in situations in which labelled data are few or nonexistent. Unsupervised feature learning and representation have seen widespread application of deep learning methods such as auto encoders and generative adversarial networks (GANs). These algorithms learn to describe the data in a hierarchical fashion, where higher-level characteristics are stacked upon lower-level ones, capturing increasingly complicated and abstract patterns as they progress. Neural networks are known as Auto encoders, and they are designed to reconstruct their input data from a compressed representation known as the latent space. The hidden layers of the network are able to learn to encode valuable characteristics that capture the underlying structure of the data when an auto encoder is trained on input that does not have labels attached to it. It is possible to use the reconstruction error as a measurement of how well the auto encoder has learned to represent the data. GANs are made up of two different types of networks: a generator network and a discriminator network. While the discriminator network is taught to differentiate between real and synthetic data, the generator network is taught to generate synthetic data samples that are an accurate representation of the real data. By going through an adversarial training process, both the generator and the discriminator are able to improve their skills. The generator is able to produce more realistic samples, and the discriminator is better able to tell the difference between real and fake samples. One meaningful representation of the data could be understood as being contained within the latent space of the generator. After the deep learning model has learned a reliable representation of the data, it can be put to use for a variety of data mining activities.
Proceedings Of Fourth International Conference On Communication Computing And Electronics Systems
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Author : V. Bindhu
language : en
Publisher: Springer Nature
Release Date : 2023-03-14
Proceedings Of Fourth International Conference On Communication Computing And Electronics Systems written by V. Bindhu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-14 with Technology & Engineering categories.
This book includes high-quality research papers presented at the Fourth International Conference on Communication, Computing and Electronics Systems (ICCCES 2022), held at the PPG Institute of Technology, Coimbatore, India, on September 15–16, 2022. The book focuses mainly on the research trends in cloud computing, mobile computing, artificial intelligence and advanced electronics systems. The topics covered are automation, VLSI, embedded systems, optical communication, RF communication, microwave engineering, artificial intelligence, deep learning, pattern recognition, communication networks, Internet of things, cyber-physical systems and healthcare informatics.
Machine Learning For Polymer Informatics
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Author : Ying Li
language : en
Publisher: American Chemical Society
Release Date : 2024-06-28
Machine Learning For Polymer Informatics written by Ying Li and has been published by American Chemical Society this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-28 with Science categories.
Machine learning has significantly accelerated the development of new polymer materials. Machine Learning for Polymer Informatics introduces the reader to the most popular ways of applying machine learning in polymer informatics. This primer will equip the reader to ask the right questions about the application of machine learning in their areas of interest, as well as critically interpret publications leveraging machine learning methods. The authors encourage readers to try machine learning techniques when they have sufficient data in their area of interest. The development of machine learning has far exceeded human imagination, and with sufficient data, everything is full of possibilities.
Benchmarks And Hybrid Algorithms In Optimization And Applications
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Author : Xin-She Yang
language : en
Publisher: Springer Nature
Release Date : 2023-08-21
Benchmarks And Hybrid Algorithms In Optimization And Applications written by Xin-She Yang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-21 with Technology & Engineering categories.
This book is specially focused on the latest developments and findings on hybrid algorithms and benchmarks in optimization and their applications in sciences, engineering, and industries. The book also provides some comprehensive reviews and surveys on implementations and coding aspects of benchmarks. The book is useful for Ph.D. students and researchers with a wide experience in the subject areas and also good reference for practitioners from academia and industrial applications.
Proceedings Of The International Field Exploration And Development Conference 2024
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Author : Jia'en Lin
language : en
Publisher: Springer Nature
Release Date : 2025-07-07
Proceedings Of The International Field Exploration And Development Conference 2024 written by Jia'en Lin and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-07 with Technology & Engineering categories.
This book compiles selected papers from the 14th International Field Exploration and Development Conference (IFEDC 2024). The work focuses on topics including Reservoir Exploration, Reservoir Drilling & Completion, Field Geophysics, Well Logging, Petroliferous Basin Evaluation, Oil & Gas Accumulation, Fine Reservoir Description, Complex Reservoir Dynamics and Analysis, Low Permeability/Tight Oil & Gas Reservoirs, Shale Oil & Gas, Fracture-Vuggy Reservoirs, Enhanced Oil Recovery in Mature Oil Fields, Enhanced Oil Recovery for Heavy Oil Reservoirs, Big Data and Artificial Intelligence, Formation Mechanisms and Prediction of Deep Carbonate Reservoirs, and other Unconventional Resources. The conference serves as a platform not only for exchanging experiences but also for advancing scientific research in oil & gas exploration and production. The primary audience for this work includes reservoir engineers, geological engineers, senior engineers, enterprise managers, and students.
Matlab For Machine Learning
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Author : Giuseppe Ciaburro
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-01-30
Matlab For Machine Learning written by Giuseppe Ciaburro and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-30 with Computers categories.
Master MATLAB tools for creating machine learning applications through effective code writing, guided by practical examples showcasing the versatility of machine learning in real-world applications Key Features Work with the MATLAB Machine Learning Toolbox to implement a variety of machine learning algorithms Evaluate, deploy, and operationalize your custom models, incorporating bias detection and pipeline monitoring Uncover effective approaches to deep learning for computer vision, time series analysis, and forecasting Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionDiscover why the MATLAB programming environment is highly favored by researchers and math experts for machine learning with this guide which is designed to enhance your proficiency in both machine learning and deep learning using MATLAB, paving the way for advanced applications. By navigating the versatile machine learning tools in the MATLAB environment, you’ll learn how to seamlessly interact with the workspace. You’ll then move on to data cleansing, data mining, and analyzing various types of data in machine learning, and visualize data values on a graph. As you progress, you’ll explore various classification and regression techniques, skillfully applying them with MATLAB functions. This book teaches you the essentials of neural networks, guiding you through data fitting, pattern recognition, and cluster analysis. You’ll also explore feature selection and extraction techniques for performance improvement through dimensionality reduction. Finally, you’ll leverage MATLAB tools for deep learning and managing convolutional neural networks. By the end of the book, you’ll be able to put it all together by applying major machine learning algorithms in real-world scenarios.What you will learn Discover different ways to transform data into valuable insights Explore the different types of regression techniques Grasp the basics of classification through Naive Bayes and decision trees Use clustering to group data based on similarity measures Perform data fitting, pattern recognition, and cluster analysis Implement feature selection and extraction for dimensionality reduction Harness MATLAB tools for deep learning exploration Who this book is for This book is for ML engineers, data scientists, DL engineers, and CV/NLP engineers who want to use MATLAB for machine learning and deep learning. A fundamental understanding of programming concepts is necessary to get started.
Machine Learning For Beginners
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Author : Dr. Urmila Mahor
language : en
Publisher: INK FREEDOM PUBLISHERS
Release Date : 2025-02-22
Machine Learning For Beginners written by Dr. Urmila Mahor and has been published by INK FREEDOM PUBLISHERS this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-22 with Antiques & Collectibles categories.
One form of artificial intelligence (AI) that enables computers to learn and make judgments without explicit programming is machine learning (ML). It entails supplying data to algorithms so they can spot trends and forecast fresh data. Natural language processing, recommender systems, and images and speech recognition are just a few of the many uses for machine learning. Traditional programming cannot solve or forecast complicated problems; machine learning can learn and train from data. It helps us make better decisions and find efficient solutions to challenging business issues. Applications of machine learning can be found in many domains, including healthcare, finance, education, sports, and more. The objectives of this book are: i. To comprehend the fundamental ideas behind machine learning. ii. Should be capable of formulating machine learning challenges that align with various applications. iii. To comprehend various machine learning algorithms and their advantages and disadvantages. iv. The capacity to use machine learning methods to resolve somewhat complex issues. v. Optimize the learnt models and report on the expected accuracy that can be attained by applying the models to a real-world problem.
Intelligent Computing An Introduction To Artificial Intelligence
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Author : Dr. Shivamurthaiah M
language : en
Publisher: Shineeks Publishers
Release Date : 2023-10-20
Intelligent Computing An Introduction To Artificial Intelligence written by Dr. Shivamurthaiah M and has been published by Shineeks Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-20 with Education categories.
In this book named ‘Intelligent Computing: An Introduction to Artificial Intelligence.’ the authors try to give detailed information on various aspects of Intelligent computing. This book consists of seven chapters from Introduction to AI to the Future of AI. The first chapter consists of the Introduction, history importance, and impact of intelligent computing in various fields. The Second chapter gives information about the Foundations of Artificial Intelligence which is cognitive science and its relation to AI. It also explains the Key concepts of Machine learning, Neural networks, Natural language processing and followed by concepts of Robotics. The third chapter explains Intelligent Computing Techniques named Supervised learning: Linear regression, Logistic regression, Support vector machines, Unsupervised learning: Clustering algorithms, Dimensionality reduction, Association rule mining, Deep learning: Neural network architectures, Convolutional neural networks, Recurrent neural networks: Generative adversarial networks, Reinforcement learning, Markov decision processes, Q-learning, Deep reinforcement learning. The fourth chapter consists of information about Applications of Intelligent Computing. Natural language processing applications: Sentiment analysis, Speech recognition, Machine translation, Computer vision applications like Object detection and recognition, Image classification, Facial recognition, Robotics applications Like Autonomous Vehicles, Industrial Automation, human robots, Healthcare applications, Disease diagnosis, Medical Image Analysis & Drug discovery. The fifth chapter consists of topics on the Ethical and Social prospective of the Implications of Intelligent Computing covers the Limitations & strengths of AI algorithms, Privacy and security concerns, Automation and its impact on job displacement also about governance and regulations on AI by the government. The sixth Chapter contains Future Directions and Challenges in Intelligent Computing Advances like interpretability of AI systems, Human-AI collaboration and augmentation, and Addressing ethical and societal challenges. The last chapter gives a conclusion about the topic: key points of AI, its Potential impact in the future & required Encouragement for further exploration of AI and intelligent computing. This book gives detailed enough information for the reader to enhance their knowledge of Intelligent Computing and AI.