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

One ingredient which is viewed as vital to the successful conduct of many large-scale numerical simulations is the ability to dynamically repartition the underlying adaptive finite element mesh among the processors so that the computations are balanced and interprocessor communication is minimized. This requires that a sequence of partitions of the computational mesh be computed during the course of the computation in which the amount of data migration necessary to realize subsequent partitions is minimized, while all of the domains of a given partition contain a roughly equal amount of computational weight. Recently, parallel multilevel graph repartitioning techniques have been developed that can quickly compute high-quality repartitions for adaptive and dynamic meshes while minimizing the amount of data which needs to be migrated between processors. These algorithms can be categorized as either schemes which compute a new partition from scratch and then intelligently remap this partition to the original partition (hereafter referred to as scratch-remap schemes), or multilevel diffusion schemes. Scratch-remap schemes work quite well for graphs which are highly imbalanced in localized areas. On slightly to moderately imbalanced graphs and those in which imbalance occurs globally throughout the graph, however, they result in excessive vertex migration compared to multilevel diffusion algorithms. On the other hand, diffusion- based schemes work well for slightly imbalanced graphs and for those in which imbalance occurs globally throughout the graph. However, these schemes perform poorly on graphs that are highly imbalanced in localized areas, as the propagation of diffusion over long distances results in excessive edge-cut and vertex migration results. In this paper, we present two new schemes for adaptive repartitioning: Locally-Matched Multilevel Scratch-Remap (or LMSR) and Wavefront Diffusion. The LMSR scheme performs purely local coarsening and partition remapping in a multilevel context. In Wavefront Diffusion, the flow of vertices move in a wavefront from overbalanced to underbalanced domains. We present experimental evaluations of our LMSR and Wavefront Diffusion algorithms on synthetically generated adaptive meshes as well as on some application meshes. We show that our LMSR algorithm decreases the amount of vertex migration required to balance the graph and produces repartitionings of similar quality compared to state-of-the-art scratch-remap schemes. Furthermore, we show that our LMSR algorithm is more scalable in terms of execution time compared to state-of-the-art scratch-remap schemes. We show that our Wavefront Diffusion algorithm obtains significantly lower vertex migration requirements, while maintaining similar edge-cut results compared to state-of-the-art multilevel diffusion algorithms, especially for highly imbalanced graphs. Furthermore, we compare Wavefront Diffusion with LMSR and show that the former will result in lower vertex migration requirements and the later will result in higher quality edge-cut results. These results hold true regardless of the distance which diffusion is required to propagate in order to balance the graph. Finally, we discuss the run times of our schemes which are both capable of repartitioning an eight million node graph in under three seconds on a 128-processor Cray T3E.

http://glaros.dtc.umn.edu/gkhome/fetch/papers/amlSC98.pdf

Dynamic Repartitioning of Adaptively Refined Meshes

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Spatial Context-Aware Networks for Mining Temporal Discriminative Period in Land Cover Detection.

This paper presents the implementation and analysis of parallel depth-first search on the ring architecture. At the heart of the parallel formulation of depth-first search is a dynamic work distribution scheme that divides the work between different processors. The effectiveness of the parallel formulation is strongly influenced by the choice of the work distribution scheme. In particular, a commonly used work distribution scheme is found to give very poor performance on large rings( < 32 processors). We present a new work distribution scheme that is better than the work distribution scheme used by other researchers, and gives good performance even on large rings (128 processors). We introduce the concept of iso-efficiency function to characterize the effectiveness of different work distribution schemes.

Parallel Depth First Search on the Ring Architecture

The authors present methods for load balancing of unstructured tree computations on large-scale SIMD (single-instruction multiple-data) machines and analyze the scalability of these and other schemes. An efficient formulation of tree search on a SIMD machine comprises two major components: (i) a triggering mechanism, which determines when the search space redistribution must occur to balance search space over processors; and (ii) a scheme to redistribute the search space. The authors devised a redistribution mechanism and a triggering mechanism. Either of these can be used in conjunction with triggering and redistribution mechanisms developed by other researchers. The authors analyze the scalability of these mechanisms. The results are verified experimentally. The analysis and experiments show that the novel load balancing methods are highly scalable on SIMD architectures. Their scalability is shown to be no worse than that of the best load balancing schemes on MIMD (multiple-instruction multiple-data) architectures. The authors verified their theoretical results by implementing the 15-puzzle problem on a CM-2 SIMD parallel computer. >

Unstructured tree search on SIMD parallel computers: a summary of results

This paper describes a new approach for clustering—pattern preserving clustering—which produces more easily interpretable and usable clusters. This approach is motivated by the following observation: while there are usually strong patterns in the data—patterns that may be key for the analysis and description of the data—these patterns are often split among different clusters by current clustering approaches. This is, perhaps, not surprising, since clustering algorithms have no built-in knowledge of these patterns and may often have goals that are in conflict with preserving patterns, e.g., minimize the distance of points to their nearest cluster centroids. In this paper, our focus is to characterize (1) the benefits of pattern preserving clustering and (2) the most effective way of performing pattern preserving clustering. To that end, we propose and evaluate two clustering algorithms, HIerarchical Clustering with pAttern Preservation (HICAP) and bisecting K-means Clustering with pAttern Preservation (K-CAP). Experimental results on document data show that HICAP can produce overlapping clusters that preserve useful patterns, but has relatively worse clustering performance than bisecting K-means with respect to the clustering evaluation criterion of entropy. By contrast, in terms of entropy, K-CAP can perform substantially better than the bisecting K-means algorithm when data sets contain clusters of widely different sizes—a common situation in the real-world. Most importantly, we also illustrate how patterns, if preserved, can aid cluster interpretation.

Vipin Kumar

Papers

Dynamic Repartitioning of Adaptively Refined Meshes

Spatial Context-Aware Networks for Mining Temporal Discriminative Period in Land Cover Detection.

Parallel Depth First Search on the Ring Architecture

Unstructured tree search on SIMD parallel computers: a summary of results

Characterizing pattern preserving clustering