This tutorial text gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches -which are based on optimization techniques - together with the Bayesian inference approach, whose essence lies in the use of a hierarchy of probabilistic models. The book presents the major machine learning methods as they have been developed in different disciplines, such as statistics, statistical and adaptive signal processing and computer science. Focusing on the physical reasoning behind the mathematics, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts. The book builds carefully from the basic classical methods to the most recent trends, with chapters written to be as self-contained as possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as short courses on sparse modeling, deep learning, and probabilistic graphical models. All major classical techniques: Mean/Least-Squares regression and filtering, Kalman filtering, stochastic approximation and online learning, Bayesian classification, decision trees, logistic regression and boosting methods. The latest trends: Sparsity, convex analysis and optimization, online distributed algorithms, learning in RKH spaces, Bayesian inference, graphical and hidden Markov models, particle filtering, deep learning, dictionary learning and latent variables modeling. Case studies - protein folding prediction, optical character recognition, text authorship identification, fMRI data analysis, change point detection, hyperspectral image unmixing, target localization, channel equalization and echo cancellation, show how the theory can be applied. MATLAB code for all the main algorithms are available on an accompanying website, enabling the reader to experiment with the code.
Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process. Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms. This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included. Deep Learning with Python also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments. What You Will Learn Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe Gain the fundamentals of deep learning with mathematical prerequisites Discover the practical considerations of large scale experiments Take deep learning models to production Who This Book Is For Software developers who want to try out deep learning as a practical solution to a particular problem. Software developers in a data science team who want to take deep learning models developed by data scientists to production.
This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of ´´boosting,´´ how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues. One chapter is dedicated to the popular genetic algorithms. This revised edition contains three entirely new chapters on critical topics regarding the pragmatic application of machine learning in industry. The chapters examine multi-label domains, unsupervised learning and its use in deep learning, and logical approaches to induction. Numerous chapters have been expanded, and the presentation of the material has been enhanced. The book contains many new exercises, numerous solved examples, thought-provoking experiments, and computer assignments for independent work.
O´Reilly´s bestselling book on Linux´s bash shell is at it again. Now that Linux is an established player both as a server and on the desktop Learning the bash Shell has been updated and refreshed to account for all the latest changes. Indeed, this third edition serves as the most valuable guide yet to the bash shell. As any good programmer knows, the first thing users of the Linux operating system come face to face with is the shell the UNIX term for a user interface to the system. In other words, it´s what lets you communicate with the computer via the keyboard and display. Mastering the bash shell might sound fairly simple but it isn´t. In truth, there are many complexities that need careful explanation, which is just what Learning the bash Shell provides. If you are new to shell programming, the book provides an excellent introduction, covering everything from the most basic to the most advanced features. And if you´ve been writing shell scripts for years, it offers a great way to find out what the new shell offers. Learning the bash Shell is also full of practical examples of shell commands and programs that will make everyday use of Linux that much easier. With this book, programmers will learn: - How to install bash as your login shell - The basics of interactive shell use, including UNIX file and directory structures, standard I/O, and background jobs - Command line editing, history substitution, and key bindings - How to customize your shell environment without programming - The nuts and bolts of basic shell programming, flow control structures, command-line options and typed variables - Process handling, from job control to processes, coroutines and subshells - Debugging techniques, such as trace and verbose modes - Techniques for implementing system-wide shell customization and features related to system security
A metaheuristic is a higher-level procedure designed to select a heuristic (partial search algorithm) that may lead to a sufficiently good solution to an optimization problem, especially with incomplete or imperfect information. The basic principle of metaheuristics is to sample a set of solutions which is large enough to be completely sampled. As metaheuristics make few assumptions about the optimization problem to be solved, they may be put to use in a variety of problems. Metaheuristics do not however, guarantee that a globally optimal solution can be found on some class of problems since most of them implement some form of stochastic optimization. Hence the solution found is often dependent on the set of random variables generated. By searching over a large set of feasible solutions, metaheuristics can often find good solutions with less computational effort than optimization algorithms, iterative methods, or simple heuristics. As such, they are useful approaches for optimization problems. Even though the metaheuristics are robust enough to yield optimum solutions, yet they often suffer from time complexity and degenerate solutions. In an effort to alleviate these problems, scientists and researchers have come up with the hybridization of the different metaheuristic approaches by conjoining with other soft computing tools and techniques to yield failsafe solutions. In a recent advancement, quantum mechanical principles are being employed to cut down the time complexity of the metaheuristic approaches to a great extent. Thus, the hybrid metaheuristic approaches have come a long way in dealing with the real life optimization problems quite successfully. Proper and faithful analysis of digital images has been in the helm of affairs in the computer vision research community given the varied amount of uncertainty inherent in digital images. Images exhibit varied uncertainty and ambiguity of information and hence understanding an image scene is far from being a general procedure. The situation becomes even graver when the images become corrupt with noise artifacts. The applications of proper analysis of images encompass a wide range of applications which include image processing, image mining, image inpainting, video surveillance, intelligent transportation systems to name a few. One of the notable areas of research in image analysis is the estimation of age progression in human beings through analysis of wrinkles in face images, which can be further utilized for tracing unknown or missing persons. Hurdle detection is one of the common tasks in robotic vision that have been done through image processing, by identifying different type of objects in the image and then calculating the distance between robot and hurdles. Image analysis has a lot to contribute in this direction. Processing of color images takes the problem of image analysis to a new dimension. Apart from processing and analysis of the color gamut which involves a lot of computational overhead, the problem also involves analysis of the varied amount of uncertainty exhibited by the color images. A video is a very fast movement of pictures. Video analysis as a part of image analysis focuses on Shot Boundary Detection (SBD), dissolve detection, detection of gradual transitions and detection of fade ins/outs. Recent trends in research on image analysis rely heavily on pose and gesture analysis. Typical applications include human-machine interaction, behavior analysis, video surveillance, annotation, search and retrieval, motion capture for the entertainment industry and interactive web-based applications. Real-time video analysis algorithms mainly focus on hand and head tracking and gesture analysis. A faithful gesture recognition algorithm can be implemented with techniques borrowed from computer vision and image processing. The evolution of the functional Magnetic Resonance Imaging (fMRI) has led to proper analysis of the study mechanisms in the brain. Several statistic
As aplicações de mineração de dados e textos oriundos da Internet têm sido alvo de recentes pesquisas. E, em todos os casos, as tarefas de mineração de dados necessitam trabalhar sobre dados limpos, consistentes e integrados para obter os melhores resultados. Sendo assim, ambientes de Data Warehouse são uma valiosa fonte de dados limpos e integrados para as aplicações de mineração. A tecnologia de Data Warehouse tem evoluído no sentido de recuperar e tratar dados provenientes da Web. Em particular, os sites de notícias são fontes ricas em textos, que podem compor um corpus linguístico. Inserindo o corpus em um ambiente de Data Warehouse, as aplicações poderão tirar proveito da flexibilidade que um modelo multidimensional e as operações OLAP fornecem. Dentre as vantagens estão a navegação pelos dados, a seleção da parte dos dados considerados relevantes, a análise dos dados em diferentes níveis de abstração, e a agregação, desagregação, rotação e filtragem sobre qualquer conjunto de dados. Este trabalho apresenta o ambiente de Data Warehouse Newsminer, que fornece um conjunto de textos consistente e limpo, na forma de um corpus multidimensional.
The paper describes the recognition of character using BAM (Bidirectional Associative Memories) and multiple feed forward Back Propagation Network (BPN) algorithm. To do this, characters are scanned first with a scanner or written in any paint program and save the characters as image. The key function of preprocessing is to improve the image in ways the characters as image. The key function of preprocessing is to improve the image in ways that increase the chances for success of the other processes. Preprocessing typically deals with techniques for enhancing contrast, removing noise. Segmentation partitions an image into its constituent parts or objects. The key role of segmentation is to extract individual characters and words from the background. Representation and description is only part of the solution for transforming raw data into a from suitable for subsequent computer processing. Feature extraction deals with extracting features that result in some quantitative information of interest or features that are basic for differentiating one class of objects from another. These characters are filtered to remove noise. In order to remove noise I have used the Spatial filtering.
This book presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks, with the theory supported by practical examples. Features: highlights how the use of deep neural networks can address new questions and protocols, as well as improve upon existing challenges in medical image computing; discusses the insightful research experience of Dr. Ronald M. Summers; presents a comprehensive review of the latest research and literature; describes a range of different methods that make use of deep learning for object or landmark detection tasks in 2D and 3D medical imaging; examines a varied selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to interleaved text and image deep mining on a large-scale radiology image database.
Machine learning methods majorly comprise image processing and soft computing methods and are mainly responsible for automation. Wheat production is influenced by assorted varying factors. A basic requisite for best production is the seed. Sorting or grading of agricultural products influenced by computer varies product wise and even product variety wise which changes region wise. Grading for new varieties released by the agricultural scientist is the major concerned as new varieties are produced by crossing existing varieties and for these proven optimized machine learning algorithms may give an adverse result. This book highlights various applications of image processing in agriculture and introduces a machine learning algorithm capable of classifying major 5 wheat cultivars (TRITICUM - DURUM: GDW 1255 (released in 2013 by ICAR) & TRITICUM - AESTVIUM: GW 273, GW 322, GW 496 & LOK 1) cultivated in Gujarat region. Experimental data consist of 11 traits comprised of shape, color & morphological characteristics. After applying feature selection algorithm,5 traits were considered & LM backpropagation was used to classify above wheat cultivars which ensued to more than 90.0% accuracy.
This successful textbook on predictive text mining offers a unified perspective on a rapidly evolving field, integrating topics spanning the varied disciplines of data science, machine learning, databases, and computational linguistics. Serving also as a practical guide, this unique book provides helpful advice illustrated by examples and case studies. This highly anticipated second edition has been thoroughly revised and expanded with new material on deep learning, graph models, mining social media, errors and pitfalls in big data evaluation, Twitter sentiment analysis, and dependency parsing discussion. The fully updated content also features in-depth discussions on issues of document classification, information retrieval, clustering and organizing documents, information extraction, web-based data-sourcing, and prediction and evaluation. Features: includes chapter summaries and exercises; explores the application of each method; provides several case studies; contains links to free text-mining software.