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

The field experiment was conducted during Rabi season in 2021-22 in randomized block design with three replications. Total 19 diverse germplasm of Amaranth were studied for 12 growth and yield parameters. Analysis of variance for 19 genotypes of Amaranthrevealed significant difference for all the 12parameters, which indicated the presence of wide spectrum of variability among the genotypes. The phenotypic coefficient of variation (PCV) was higher than the respective genotypic coefficient of variation (GCV) for all the traits. High heritability and genetic advance as per cent of mean were observed for all twelve characters. The highest heritability was recorded in biological yield per plant 99.49%and lowest for days to germination 69.95%. Correlation coefficient studies indicated that genotypic correlation coefficient was found to be higher than phenotypic correlation coefficients for most of the characters, indicating a strong inherent association between various characters and due to which it is affected by environmental components in regard to phenotypic expression. Seed yield expressed highly significant and positive correlation with plant height and biological yield per plant at both genotypic and phenotypic level, which implies that these characters were the primer contributing factors to seed yield. All the combination of traits should be considered, while breeding programme for selecting high yielding genotypes and suitable for breeders to achieving improved plant type. Path coefficient analysis revealed that highest positive direct effect on seed yield kg per ha was observed for biological yield per plant, seed yield per plant, number of leaves per plant, days to maturity, days to germination, inflorescence length, fresh leaf weight and plant height. Improvement of these characters might be improved.

Genetic Variability, Heritability, Correlation and Path Coefficient Analysis in Amaranth (Amaranthus spp.) in Western Uttar Pradesh (India)

Finding Novel Multivariate Relationships in Time Series Data: Applications to Climate and Neuroscience

Percutaneous nephrolithotomy is the standard procedure used to treat nephrolithiasis. Glidewires play a pivotal role in the smooth conduction of the procedure. Rough handling of glidewires and other accessories can lead to their fragmentation or stripping thus complicating the procedure. These foreign bodies can get easily lost in the retroperitoneum and are difficult to retrieve. We present a case of stripping of glidewire during percutaneous nephrolithotomy and the procedure to retrieve it percutaneously under fluoroscopic guidance. 

https://doi.org/10.1016/j.eucr.2022.102131

Stripping of glidewire - A rare intraoperative complication of Percutaneous Nephrolithotomy

Deep Learning for Air Quality Prediction after COVID-19 Pandemic Based on Pollutant and Meteorological Data

A Field experiment was conducted during 2005-06 to test the suitability and adaptability of cotton, potato and wheat genotypes for cotton based system. Highest seed-cotton yield was recorded in LHH 144 (2.3-2.6 t ha -1 ) followed by Ankur 651 (2.2 t ha -1 ) and lowest in CNH 36 (1.9 t ha -1 ). This was associated with boll weight, which was significantly higher in LHH 144. Among potato genotypes, Kufri Pukhraj had significantly higher tuber yield and total biomass productivity than Kufri Surya, largely due to higher LAI in Kufri Pukhraj. However, tuber dry matter (%) and photosynthesis rate were invariably higher in Kufri Surya (> 19% and 20.6 μmol CO 2 m -2 S -1 , respectively) than in Kufri Pukhraj (< 16% and 19.4 pmol CO 2 m -2 S -1 ). In wheat, grain yield was significantly reduced in PBW 343 (13 %) under late sowing (40 days) after the harvest of potato as compared with the normal sowing. This was mainly due to reduction in biomass and leaf area. The results indicate that early maturing cotton genotypes (Ankur 651 and LHH 144) and potato genotypes (Kufri Pukhraj and Kufri Surya) could provide opportunities for the intensification of cotton-wheat system by inclusion of potato in northwestern plains in system mode.

Vipin Kumar

Papers

Genetic Variability, Heritability, Correlation and Path Coefficient Analysis in Amaranth (Amaranthus spp.) in Western Uttar Pradesh (India)

Finding Novel Multivariate Relationships in Time Series Data: Applications to Climate and Neuroscience

Stripping of glidewire - A rare intraoperative complication of Percutaneous Nephrolithotomy

Deep Learning for Air Quality Prediction after COVID-19 Pandemic Based on Pollutant and Meteorological Data

Inclusion of potato for intensification of cotton-wheat cropping system in northwestern plains : a physiological study