Deep Learning For Data Mining Unsupervised Feature Learning And Representation
DOWNLOAD
Download Deep Learning For Data Mining Unsupervised Feature Learning And Representation PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Deep Learning For Data Mining Unsupervised Feature Learning And Representation book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Deep Learning For Data Mining Unsupervised Feature Learning And Representation
DOWNLOAD
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.
Deep Learning For Data Mining Unsupervised Feature Learning And Representation
DOWNLOAD
Author : Komal
language : en
Publisher: Xoffencer International Book Publication House
Release Date : 2024-12-30
Deep Learning For Data Mining Unsupervised Feature Learning And Representation written by Komal and has been published by Xoffencer International Book Publication House this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-30 with Computers categories.
Data mining is the process of discovering patterns, correlations, and insights from large sets of data using various analytical techniques. It plays a crucial role in transforming raw data into meaningful information, which can then be used for decision-making, predictions, and insights in various fields such as business, healthcare, finance, and more. The most commonly used data mining techniques include classification, clustering, association, regression, anomaly detection, and sequential pattern mining. Each of these techniques has its own strengths and applications depending on the type of data and the goals of the analysis. Classification is one of the most popular techniques used in data mining. It involves categorizing data into predefined classes based on certain attributes. Algorithms such as decision trees, random forests, support vector machines, and neural networks are widely used for classification tasks. For instance, in the healthcare industry, classification techniques can be used to predict whether a patient is likely to develop a certain disease based on historical medical data. This technique works by training a model on a labeled dataset, where the outcome is known, and then using that model to classify new, unlabeled data into one of the predefined categories. Clustering is another essential data mining technique, where the goal is to group similar data points into clusters or segments. Unlike classification, clustering is an unsupervised learning method, meaning it doesn’t rely on predefined labels. Instead, it seeks to identify natural groupings in the data. Clustering algorithms like k-means, hierarchical clustering, and DBSCAN are commonly used. This technique is widely applied in market segmentation, where businesses group customers with similar behavior or preferences into clusters to better target marketing efforts. Clustering can also be useful in anomaly detection, where outliers that don’t fit well into any cluster can signal potential fraud or irregular behavior.
Deep Learning For The Earth Sciences
DOWNLOAD
Author : Gustau Camps-Valls
language : en
Publisher: John Wiley & Sons
Release Date : 2021-08-18
Deep Learning For The Earth Sciences written by Gustau Camps-Valls and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-18 with Technology & Engineering categories.
DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferated. Deep Learning for the Earth Sciences delivers a unique perspective and treatment of the concepts, skills, and practices necessary to quickly become familiar with the application of deep learning techniques to the Earth sciences. The book prepares readers to be ready to use the technologies and principles described in their own research. The distinguished editors have also included resources that explain and provide new ideas and recommendations for new research especially useful to those involved in advanced research education or those seeking PhD thesis orientations. Readers will also benefit from the inclusion of: An introduction to deep learning for classification purposes, including advances in image segmentation and encoding priors, anomaly detection and target detection, and domain adaptation An exploration of learning representations and unsupervised deep learning, including deep learning image fusion, image retrieval, and matching and co-registration Practical discussions of regression, fitting, parameter retrieval, forecasting and interpolation An examination of physics-aware deep learning models, including emulation of complex codes and model parametrizations Perfect for PhD students and researchers in the fields of geosciences, image processing, remote sensing, electrical engineering and computer science, and machine learning, Deep Learning for the Earth Sciences will also earn a place in the libraries of machine learning and pattern recognition researchers, engineers, and scientists.
Unsupervised Feature Learning Via Sparse Hierarchical Representations
DOWNLOAD
Author : Honglak Lee
language : en
Publisher: Stanford University
Release Date : 2010
Unsupervised Feature Learning Via Sparse Hierarchical Representations written by Honglak Lee and has been published by Stanford University this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with categories.
Machine learning has proved a powerful tool for artificial intelligence and data mining problems. However, its success has usually relied on having a good feature representation of the data, and having a poor representation can severely limit the performance of learning algorithms. These feature representations are often hand-designed, require significant amounts of domain knowledge and human labor, and do not generalize well to new domains. To address these issues, I will present machine learning algorithms that can automatically learn good feature representations from unlabeled data in various domains, such as images, audio, text, and robotic sensors. Specifically, I will first describe how efficient sparse coding algorithms --- which represent each input example using a small number of basis vectors --- can be used to learn good low-level representations from unlabeled data. I also show that this gives feature representations that yield improved performance in many machine learning tasks. In addition, building on the deep learning framework, I will present two new algorithms, sparse deep belief networks and convolutional deep belief networks, for building more complex, hierarchical representations, in which more complex features are automatically learned as a composition of simpler ones. When applied to images, this method automatically learns features that correspond to objects and decompositions of objects into object-parts. These features often lead to performance competitive with or better than highly hand-engineered computer vision algorithms in object recognition and segmentation tasks. Further, the same algorithm can be used to learn feature representations from audio data. In particular, the learned features yield improved performance over state-of-the-art methods in several speech recognition tasks.
Deep Learning For Data Mining Unsupervised Feature Learning And Representation
DOWNLOAD
Author : Srinivas Babu Ratnam
language : en
Publisher: Xoffencerpublication
Release Date : 2023-07-03
Deep Learning For Data Mining Unsupervised Feature Learning And Representation written by Srinivas Babu Ratnam and has been published by Xoffencerpublication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-03 with Computers categories.
Several empirical research have come to the conclusion that the representation of data plays a vital role in the efficiency with which machine learning algorithms complete their tasks. This indicates that the design of feature extraction, preprocessing, and data transformations requires a disproportionate amount of time and resources when actually executing machine learning algorithms. These steps include preparing the data for analysis, extracting features from the data, and processing the data. This is because each of these components is essential to the algorithm as a whole in order for it to function properly. In spite of the fact that it is of the utmost significance, feature engineering calls for a significant amount of human effort. It also shows a shortcoming of the learning algorithms that are now in use, which is their inability to extract all of the pertinent characteristics from the data that is currently accessible. This is a difficulty with the approaches that are currently utilized in the process of learning. An approach that may be utilized to make up for such a shortfall is called feature engineering, and it involves making use of human intelligence in conjunction with prior information. It would be extremely desired to make learning algorithms less dependent on feature engineering in order to expedite the production of innovative applications and, more crucially, to realize advancements in artificial intelligence (AI). This would be done in order to achieve developments in AI. There are two possible consequences resulting from this. This would make it possible to use machine learning in a larger variety of applications that are simpler to put into action, which would increase the value of machine learning. An artificial intelligence has to have at least a fundamental comprehension of the environment in which humans live, and this may be accomplished if a learner is able to interpret the concealed explanatory factors that are embedded within the visible milieu of low-level sensory input. It is conceivable to combine feature engineering with feature learning in order to obtain state-of-the-art solutions that can be applied to actual circumstances in the real world.
Deep Learning
DOWNLOAD
Author : Li Deng
language : en
Publisher:
Release Date : 2014
Deep Learning written by Li Deng and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with Machine learning categories.
Provides an overview of general deep learning methodology and its applications to a variety of signal and information processing tasks
Machine Learning Optimization And Data Science
DOWNLOAD
Author : Giuseppe Nicosia
language : en
Publisher: Springer Nature
Release Date : 2021-01-06
Machine Learning Optimization And Data Science written by Giuseppe Nicosia and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-06 with Computers categories.
This two-volume set, LNCS 12565 and 12566, constitutes the refereed proceedings of the 6th International Conference on Machine Learning, Optimization, and Data Science, LOD 2020, held in Siena, Italy, in July 2020. The total of 116 full papers presented in this two-volume post-conference proceedings set was carefully reviewed and selected from 209 submissions. These research articles were written by leading scientists in the fields of machine learning, artificial intelligence, reinforcement learning, computational optimization, and data science presenting a substantial array of ideas, technologies, algorithms, methods, and applications.
Data Engineering And Applications
DOWNLOAD
Author : Sanjeev Sharma
language : en
Publisher: Springer Nature
Release Date : 2022-10-11
Data Engineering And Applications written by Sanjeev Sharma and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-11 with Computers categories.
The book contains select proceedings of the 3rd International Conference on Data, Engineering, and Applications (IDEA 2021). It includes papers from experts in industry and academia that address state-of-the-art research in the areas of big data, data mining, machine learning, data science, and their associated learning systems and applications. This book will be a valuable reference guide for all graduate students, researchers, and scientists interested in exploring the potential of big data applications.
Machine Learning For Data Science Handbook
DOWNLOAD
Author : Lior Rokach
language : en
Publisher: Springer Nature
Release Date : 2023-08-17
Machine Learning For Data Science Handbook written by Lior Rokach and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-17 with Computers categories.
This book organizes key concepts, theories, standards, methodologies, trends, challenges and applications of data mining and knowledge discovery in databases. It first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. It also gives in-depth descriptions of data mining applications in various interdisciplinary industries.
The Era Of Artificial Intelligence Machine Learning And Data Science In The Pharmaceutical Industry
DOWNLOAD
Author : Stephanie K. Ashenden
language : en
Publisher: Academic Press
Release Date : 2021-04-23
The Era Of Artificial Intelligence Machine Learning And Data Science In The Pharmaceutical Industry written by Stephanie K. Ashenden and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-23 with Computers categories.
The Era of Artificial Intelligence, Machine Learning and Data Science in the Pharmaceutical Industry examines the drug discovery process, assessing how new technologies have improved effectiveness. Artificial intelligence and machine learning are considered the future for a wide range of disciplines and industries, including the pharmaceutical industry. In an environment where producing a single approved drug costs millions and takes many years of rigorous testing prior to its approval, reducing costs and time is of high interest. This book follows the journey that a drug company takes when producing a therapeutic, from the very beginning to ultimately benefitting a patient's life. This comprehensive resource will be useful to those working in the pharmaceutical industry, but will also be of interest to anyone doing research in chemical biology, computational chemistry, medicinal chemistry and bioinformatics. - Demonstrates how the prediction of toxic effects is performed, how to reduce costs in testing compounds, and its use in animal research - Written by the industrial teams who are conducting the work, showcasing how the technology has improved and where it should be further improved - Targets materials for a better understanding of techniques from different disciplines, thus creating a complete guide