Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Deep Learning

There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data

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Data Mining: Concepts and Techniques

A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold. For example, we sometimes have a classification task in one domain of interest, but we only have sufficient training data in another domain of interest, where the latter data may be in a different feature space or follow a different data distribution. In such cases, knowledge transfer, if done successfully, would greatly improve the performance of learning by avoiding much expensive data-labeling efforts. In recent years, transfer learning has emerged as a new learning framework to address this problem. This survey focuses on categorizing and reviewing the current progress on transfer learning for classification, regression, and clustering problems. In this survey, we discuss the relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift. We also explore some potential future issues in transfer learning research.

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A Survey on Transfer Learning

COPD is the third leading cause of death worldwide and the second leading cause of death in India. There are so many factors that contribute to the
development of COPD and determine its progression and severity. This study was done to know the Sociodemographic and clinical proles of smoker
and non-smoker COPD patients attending a tertiary care centre in North India. This was a hospital-based prospective andMaterial and Method:
observational study conducted in the Department of TB and Respiratory Diseases, S.N. Medical College, Agra. Patients were recruited for the study
based on GOLD criteria (Post Bronchodilator FEV1/FVC < 0.7). Patients were evaluated based on demographic characteristics and clinical features.
Results: Among the total eligible patient's prevalence of smoker COPD patients was 62.96% and the prevalence of non-smoker patients was 37.04%.
Among the eligible 108 cohort patients, 70 (64.81%) were males and 38 (35.18%) were females. Female patients were higher in the non-smoker group
(75%). The higher number of patients enrolled were from a rural background (60.18%). Mean age of the population was 48.8 years with a majority of
patients lying in the younger age group and were malnourished (BMI<18.5). Breathlessness was the most common symptom reported (49%) and
smoker COPD patients were having severe dyspnoea (46.29%). 37.04% prevalence of non-smoker COPD was noted. Early detectionConclusion:
with the help of spirometry will be helpful in not only modifying the disease course but also delaying and preventing fatal complications. Hence, patients
may be treated earliest by various measures like lifestyle modication, smoking cessation, etc.

Study of sociodemographic and clinical profile of smoker and
non-smoker copd patients attending a tertiary care centre in north india

Present study consists the overall development aspects of an Autonomous Underwater Vehicle (AUV) such as mechanical design, modelling, software architecture, controllers and navigation in combination with the lake experiments conducted on the vehicle at a shallow depth. AUV prototype in discussion is designated, AUV-150 and is designed to operate at a depth of 150 meters. It is a cylindrical-shaped carrier with streamlined fairing to reduce hydrodynamic drag. It is embedded with active propulsion, navigation, and control systems. Propulsion system comprises thrusters for generating motion in different directions to control surge, sway, heave, pitch, and yaw. Two arrays of cross-fins have also been fixed at the two ends to provide additional stability to the AUV against roll. A lithium polymer battery powers the vehicle and a pressure hull contains its electronics and energy system. Equipped with a camera, CTD and side scan sonar as payload sensors the AUV-150 is perfectly designed for performing underwater terrain mapping as well as oceanographic survey activities. The experimental results obtained from the shallow depth operation are quite satisfactory from the operational point of view.

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An approach towards the design and development of a flexible 5dof AUV

We present two new parallel algorithms QSP1 and QSP2 based on sequential quicksort for sorting data on a mesh multicomputer, and analyze their scalability using the isoefficiency metric. We show that QSP2 matches the lower bound on the isoefficiency function for mesh multicomputers, while QSP1 is fairly close to optimal. Lang et al. (1l and Schnorr et aL (2) have developed parallel sorting algorithms for the mesh architecture that have either optimal (Schnorr) or close to optimal (Lang) run-time complexity for the one-element-perprocessor case. Both QSP1 and QSP2 have better scalability than the scaleddown variants of these algorithms (for the case in which there are more elements than processors). We also analyze a different variant of Lang's sort which is as scalable as QSP2. We briefly discuss another metric called "resource consumption." According to this metric, both QSP1 and QSP2 are superior to variants of Lang's sort.

http://rd.springer.com/content/pdf/10.1007%2FBF01407839.pdf

Efficient Algorithms for Parallel on Mesh Multicomputers

The problem of node classification in networks is an important one in a wide variety of social networking domains. In many real applications such as product recommendations, the class of interest may be very rare. In such scenarios, it is often very difficult to learn the most relevant node classification characteristics, both because of the paucity of training data, and because of poor connectivity among rare class nodes in the network structure. Node classification methods crucially dependent upon structural homophily, and a lack of connectivity among rare class nodes can create significant challenges. However, many such social networks are content-rich, and the content-rich nature of such networks can be leveraged to compensate for the lack of structural connectivity among rare class nodes. While content-centric and semi-supervised methods have been used earlier in the context of paucity of labeled data, the rare class scenario has not been investigated in this context. In fact, we are not aware of any known classification method which is tailored towards rare class detection in networks. This paper will present a spectral approach for rare-class detection, which uses a distance-preserving transform, in order to combine the structural information in the network with the available content. We will show the advantage of this approach over traditional methods for collective classification.

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Rare class detection in networks

Collecting large annotated datasets in Remote Sensing is often expensive and thus can become a major obstacle for training advanced machine learning models. Common techniques of addressing this issue, based on the underlying idea of pre-training the Deep Neural Networks (DNN) on freely available large datasets, cannot be used for Remote Sensing due to the unavailability of such large-scale labeled datasets and the heterogeneity of data sources caused by the varying spatial and spectral resolution of different sensors. Self-supervised learning is an alternative approach that learns feature representation from unlabeled images without using any human annotations. In this paper, we introduce a new method for land cover mapping by using a clustering based pretext task for self-supervised learning. We demonstrate the effectiveness of the method on two societally relevant applications from the aspect of segmentation performance, discriminative feature representation learning and the underlying cluster structure. We also show the effectiveness of the active sampling using the clusters obtained from our method in improving the mapping accuracy given a limited budget of annotating.

Vipin Kumar

Papers

Study of sociodemographic and clinical profile of smoker and non-smoker copd patients attending a tertiary care centre in north india

An approach towards the design and development of a flexible 5dof AUV

Efficient Algorithms for Parallel on Mesh Multicomputers

Rare class detection in networks

Clustering augmented Self-Supervised Learning: Anapplication to Land Cover Mapping