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

Genetic Interaction (GI) data provides a means for exploring the structure and function of pathways in a cell. Coherent value bicliques (submatrices) in GI data represents functionally similar gene modules or protein complexes. However, no systematic approach has been proposed for exhaustively enumerating all coherent value submatrices in such data sets, which is the problem addressed in this paper. Using a monotonic range measure to capture the coherence of values in a submatrix of an input data matrix, we propose a two-step Apriori-based algorithm for discovering all nearly constant value submatrices, referred to as Range Constrained Blocks. By systematic evaluation on an extensive genetic interaction data set, we show that the coherent value submatrices represent groups of genes that are functionally related than the submatrices with diverse values. We also show that our approach can exhaustively find all the submatrices with a range less than a given threshold, while the other competing approaches can not find all such submatrices.

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Discovering coherent value bicliques in genetic interaction data

Test generation for combinational circuits is an important step in the VLSI design process. Unfortunately, the problem is highly computation-intensive and for circuits encountered in practice, test generation time can often be enormous. In this paper, we present a parallel formulation of the backtrack search algorithm called PODEM, which is a highly used algorithm for this problem. It is known that the sequential PODEM algorithm consumes most of its execution time in generating tests for 'hard-to-detect' (HTD) faults and is often unable to detect them even after a large number of backtracks. Our parallel formulation overcomes these limitations by dividing the search space and searching it concurrently using multiple processes. We present a number of experimental results and show that these match our theoretical results presented elsewhere. We show that the search efficiency of the parallel algorithm improves and even beats that of the sequential algorithm as the 'hardness' of a fault increases. We present speedup results and performance analyses of our formulation on a 128 processor Symult s2010 multicomputer. We also present preliminary results on a network of Sun workstations. Our results show that parallel search techniques provides good speedups as well as high fault coverage of the HTD faults in reasonable time when compared to the uniprocessor implementation. Our experimental validation of most of our theoretical results builds confidence in the following theoretical prediction: our parallel formulation of PODEM is highly scalable on a variety of commercially-available, large MIMD parallel processors (in additions to the ones with which we experimented).

Paper: Automatic test pattern generation on parallel processors

The problem of mining hidden associations present in the large amounts of data has seen widespread applications in many practical domains such as customer-oriented planning and marketing, telecommunication network monitoring, and analyzing data from scientific experiments. The combinatorial complexity of the problem and phenomenal growth in the sizes of available datasets motivate the need for efficient and scalable parallel algorithms. The design of such algorithms is challenging. This chapter presents an evolutionary and comparative review of many existing representative serial and parallel algorithms for discovering two kinds of associations. The first part of the chapter is devoted to the non-sequential associations, which utilize the relationships between events that happen together. The second part is devoted to the more general and potentially more useful sequential associations, which utilize the temporal or sequential relationships between events. It is shown that many existing algorithms actually belong to a few categories which are decided by the broader design strategies. Overall the aim of the chapter is to provide a comprehensive account of the challenges and issues involved in effective parallel formulations of algorithms for discovering associations, and how various existing algorithms try to handle them.

Efficient Parallel Algorithms for Mining Associations

A parallel formulation of a branch-and-bound algorithm for floorplan optimization in VLSI design is given. Implementation details and performance analyses of the authors' formulation are presented. This implementation was done on a 128-node Symult s2010 multicomputer, as well as on a network of 16 SUN workstations. In the experiments with realistic problems, linear speedups on both machine configurations were obtained. Previous analyses of a similar parallel algorithm suggest that linear speedup can be obtained even on very large computers (>1000 processors) on practical instances of this problem. >

Floorplan optimization on multiprocessors

Data mining is frequently obstructed by privacy concerns. In many cases data is distributed, and bringing the data together in one place for analysis is not possible due to privacy laws (e.g. HIPAA) or policies. Privacy preserving data mining techniques have been developed to address this issue by providing mechanisms to mine the data while giving certain privacy guarantees. In this work we address the issue of privacy preserving nearest neighbor search, which forms the kernel of many data mining applications. To this end, we present a novel algorithm based on secure multiparty computation primitives to compute the nearest neighbors of records in horizontally distributed data. We show how this algorithm can be used in three important data mining algorithms, namely LOF outlier detection, SNN clustering, and kNN classification.

Vipin Kumar

Papers

Discovering coherent value bicliques in genetic interaction data

Paper: Automatic test pattern generation on parallel processors

Efficient Parallel Algorithms for Mining Associations

Floorplan optimization on multiprocessors

Privacy Preserving Nearest Neighbor Search