Deep Learning For Data Mining Unsupervised Feature Learning And Representation
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DEEP LEARNING FOR DATA MINING: UNSUPERVISED FEATURE LEARNING AND REPRESENTATION
Author: Mr. Srinivas Rao Adabala
language: en
Publisher: Xoffencerpublication
Release Date: 2023-08-14
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
Author: Komal
language: en
Publisher: Xoffencer International Book Publication House
Release Date: 2024-12-30
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
Author: Gustau Camps-Valls
language: en
Publisher: John Wiley & Sons
Release Date: 2021-08-18
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.