Powerful smart applications using deep learning algorithms to dominate numerical computing, deep learning, and functional programming. Key Features Explore machine learning techniques with prominent open source Scala libraries such as Spark ML, H2O, MXNet, Zeppelin, and DeepLearning4j Solve real-world machine learning problems by delving complex numerical computing with Scala functional programming in a scalable and faster way Cover all key aspects such as collection, storing, processing, analyzing, and evaluation required to build and deploy machine models on computing clusters using Scala Play framework. Book Description Machine learning has had a huge impact on academia and industry by turning data into actionable information. Scala has seen a steady rise in adoption over the past few years, especially in the fields of data science and analytics. This book is for data scientists, data engineers, and deep learning enthusiasts who have a background in complex numerical computing and want to know more hands-on machine learning application development. If you're well versed in machine learning concepts and want to expand your knowledge by delving into the practical implementation of these concepts using the power of Scala, then this book is what you need! Through 11 end-to-end projects, you will be acquainted with popular machine learning libraries such as Spark ML, H2O, DeepLearning4j, and MXNet. At the end, you will be able to use numerical computing and functional programming to carry out complex numerical tasks to develop, build, and deploy research or commercial projects in a production-ready environment. What you will learn Apply advanced regression techniques to boost the performance of predictive models Use different classification algorithms for business analytics Generate trading strategies for Bitcoin and stock trading using ensemble techniques Train Deep Neural Networks (DNN) using H2O and Spark ML Utilize NLP to build scalable machine learning models Learn how to apply reinforcement learning algorithms such as Q-learning for developing ML application Learn how to use autoencoders to develop a fraud detection application Implement LSTM and CNN models using DeepLearning4j and MXNet Who this book is for If you want to leverage the power of both Scala and Spark to make sense of Big Data, then this book is for you. If you are well versed with machine learning concepts and wants to expand your knowledge by delving into the practical implementation using the power of Scala, then this book is what you need! Strong understanding of Scala Programming language is recommended. Basic familiarity with machine Learning techniques will be more helpful.
Build and deploy powerful neural network models using the latest Java deep learning libraries Key Features Understand DL with Java by implementing real-world projects Master implementations of various ANN models and build your own DL systems Develop applications using NLP, image classification, RL, and GPU processing Book Description Java is one of the most widely used programming languages. With the rise of deep learning, it has become a popular choice of tool among data scientists and machine learning experts. Java Deep Learning Projects starts with an overview of deep learning concepts and then delves into advanced projects. You will see how to build several projects using different deep neural network architectures such as multilayer perceptrons, Deep Belief Networks, CNN, LSTM, and Factorization Machines. You will get acquainted with popular deep and machine learning libraries for Java such as Deeplearning4j, Spark ML, and RankSys and you’ll be able to use their features to build and deploy projects on distributed computing environments. You will then explore advanced domains such as transfer learning and deep reinforcement learning using the Java ecosystem, covering various real-world domains such as healthcare, NLP, image classification, and multimedia analytics with an easy-to-follow approach. Expert reviews and tips will follow every project to give you insights and hacks. By the end of this book, you will have stepped up your expertise when it comes to deep learning in Java, taking it beyond theory and be able to build your own advanced deep learning systems. What you will learn Master deep learning and neural network architectures Build real-life applications covering image classification, object detection, online trading, transfer learning, and multimedia analytics using DL4J and open-source APIs Train ML agents to learn from data using deep reinforcement learning Use factorization machines for advanced movie recommendations Train DL models on distributed GPUs for faster deep learning with Spark and DL4J Ease your learning experience through 69 FAQs Who this book is for If you are a data scientist, machine learning professional, or deep learning practitioner keen to expand your knowledge by delving into the practical aspects of deep learning with Java, then this book is what you need! Get ready to build advanced deep learning models to carry out complex numerical computations. Some basic understanding of machine learning concepts and a working knowledge of Java are required.
Implement popular deep learning techniques to make your IoT applications smarter Key FeaturesUnderstand how deep learning facilitates fast and accurate analytics in IoTBuild intelligent voice and speech recognition apps in TensorFlow and ChainerAnalyze IoT data for making automated decisions and efficient predictionsBook Description Artificial Intelligence is growing quickly, which is driven by advancements in neural networks(NN) and deep learning (DL). With an increase in investments in smart cities, smart healthcare, and industrial Internet of Things (IoT), commercialization of IoT will soon be at peak in which massive amounts of data generated by IoT devices need to be processed at scale. Hands-On Deep Learning for IoT will provide deeper insights into IoT data, which will start by introducing how DL fits into the context of making IoT applications smarter. It then covers how to build deep architectures using TensorFlow, Keras, and Chainer for IoT. You’ll learn how to train convolutional neural networks(CNN) to develop applications for image-based road faults detection and smart garbage separation, followed by implementing voice-initiated smart light control and home access mechanisms powered by recurrent neural networks(RNN). You’ll master IoT applications for indoor localization, predictive maintenance, and locating equipment in a large hospital using autoencoders, DeepFi, and LSTM networks. Furthermore, you’ll learn IoT application development for healthcare with IoT security enhanced. By the end of this book, you will have sufficient knowledge need to use deep learning efficiently to power your IoT-based applications for smarter decision making. What you will learnGet acquainted with different neural network architectures and their suitability in IoTUnderstand how deep learning can improve the predictive power in your IoT solutionsCapture and process streaming data for predictive maintenanceSelect optimal frameworks for image recognition and indoor localizationAnalyze voice data for speech recognition in IoT applicationsDevelop deep learning-based IoT solutions for healthcareEnhance security in your IoT solutionsVisualize analyzed data to uncover insights and perform accurate predictionsWho this book is for If you’re an IoT developer, data scientist, or deep learning enthusiast who wants to apply deep learning techniques to build smart IoT applications, this book is for you. Familiarity with machine learning, a basic understanding of the IoT concepts, and some experience in Python programming will help you get the most out of this book.
Supervised and unsupervised machine learning made easy in Scala with this quick-start guide. Key FeaturesConstruct and deploy machine learning systems that learn from your data and give accurate predictionsUnleash the power of Spark ML along with popular machine learning algorithms to solve complex tasks in Scala.Solve hands-on problems by combining popular neural network architectures such as LSTM and CNN using Scala with DeepLearning4j libraryBook Description Scala is a highly scalable integration of object-oriented nature and functional programming concepts that make it easy to build scalable and complex big data applications. This book is a handy guide for machine learning developers and data scientists who want to develop and train effective machine learning models in Scala. The book starts with an introduction to machine learning, while covering deep learning and machine learning basics. It then explains how to use Scala-based ML libraries to solve classification and regression problems using linear regression, generalized linear regression, logistic regression, support vector machine, and Naïve Bayes algorithms. It also covers tree-based ensemble techniques for solving both classification and regression problems. Moving ahead, it covers unsupervised learning techniques, such as dimensionality reduction, clustering, and recommender systems. Finally, it provides a brief overview of deep learning using a real-life example in Scala. What you will learnGet acquainted with JVM-based machine learning libraries for Scala such as Spark ML and Deeplearning4jLearn RDDs, DataFrame, and Spark SQL for analyzing structured and unstructured dataUnderstand supervised and unsupervised learning techniques with best practices and pitfallsLearn classification and regression analysis with linear regression, logistic regression, Naïve Bayes, support vector machine, and tree-based ensemble techniques Learn effective ways of clustering analysis with dimensionality reduction techniquesLearn recommender systems with collaborative filtering approachDelve into deep learning and neural network architecturesWho this book is for This book is for machine learning developers looking to train machine learning models in Scala without spending too much time and effort. Some fundamental knowledge of Scala programming and some basics of statistics and linear algebra is all you need to get started with this book.
Learn how to solve real life problems using different methods like logic regression, random forests and SVM’s with TensorFlow. Key Features Understand predictive analytics along with its challenges and best practices Embedded with assessments that will help you revise the concepts you have learned in this book Book Description Predictive analytics discovers hidden patterns from structured and unstructured data for automated decision making in business intelligence. Predictive decisions are becoming a huge trend worldwide, catering to wide industry sectors by predicting which decisions are more likely to give maximum results. TensorFlow, Google’s brainchild, is immensely popular and extensively used for predictive analysis. This book is a quick learning guide on all the three types of machine learning, that is, supervised, unsupervised, and reinforcement learning with TensorFlow. This book will teach you predictive analytics for high-dimensional and sequence data. In particular, you will learn the linear regression model for regression analysis. You will also learn how to use regression for predicting continuous values. You will learn supervised learning algorithms for predictive analytics. You will explore unsupervised learning and clustering using K-meansYou will then learn how to predict neighborhoods using K-means, and then, see another example of clustering audio clips based on their audio features. This book is ideal for developers, data analysts, machine learning practitioners, and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow. This book is embedded with useful assessments that will help you revise the concepts you have learned in this book. What you will learn Learn TensorFlow features in a real-life problem, followed by detailed TensorFlow installation and configuration Explore computation graphs, data, and programming models also get an insight into an example of implementing linear regression model for predictive analytics Solve the Titanic survival problem using logistic regression, random forests, and SVMs for predictive analytics Dig deeper into predictive analytics and find out how to take advantage of it to cluster records belonging to the certain group or class for a dataset of unsupervised observations Learn several examples of how to apply reinforcement learning algorithms for developing predictive models on real-life datasets Who this book is for This book is aimed at developers, data analysts, machine learning practitioners, and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow.
One stop guide to implementing award-winning, and cutting-edge CNN architectures Key Features Fast-paced guide with use cases and real-world examples to get well versed with CNN techniques Implement CNN models on image classification, transfer learning, Object Detection, Instance Segmentation, GANs and more Implement powerful use-cases like image captioning, reinforcement learning for hard attention, and recurrent attention models Book Description Convolutional Neural Network (CNN) is revolutionizing several application domains such as visual recognition systems, self-driving cars, medical discoveries, innovative eCommerce and more.You will learn to create innovative solutions around image and video analytics to solve complex machine learning and computer vision related problems and implement real-life CNN models. This book starts with an overview of deep neural networkswith the example of image classification and walks you through building your first CNN for human face detector. We will learn to use concepts like transfer learning with CNN, and Auto-Encoders to build very powerful models, even when not much of supervised training data of labeled images is available. Later we build upon the learning achieved to build advanced vision related algorithms for object detection, instance segmentation, generative adversarial networks, image captioning, attention mechanisms for vision, and recurrent models for vision. By the end of this book, you should be ready to implement advanced, effective and efficient CNN models at your professional project or personal initiatives by working on complex image and video datasets. What you will learn From CNN basic building blocks to advanced concepts understand practical areas they can be applied to Build an image classifier CNN model to understand how different components interact with each other, and then learn how to optimize it Learn different algorithms that can be applied to Object Detection, and Instance Segmentation Learn advanced concepts like attention mechanisms for CNN to improve prediction accuracy Understand transfer learning and implement award-winning CNN architectures like AlexNet, VGG, GoogLeNet, ResNet and more Understand the working of generative adversarial networks and how it can create new, unseen images Who this book is for This book is for data scientists, machine learning and deep learning practitioners, Cognitive and Artificial Intelligence enthusiasts who want to move one step further in building Convolutional Neural Networks. Get hands-on experience with extreme datasets and different CNN architectures to build efficient and smart ConvNet models. Basic knowledge of deep learning concepts and Python programming language is expected.
Scientific Essay from the year 2007 in the subject Leadership and Human Resources - Miscellaneous, University of Leeds (Leeds University Business School), course: MA HRM, language: English, abstract: Performance appraisal (PA) is a process of setting some targets for the individuals which they are required to achieve. Performance of individuals is evaluated by the process and good performance is acknowledged by rewarding them which may be financial rise in pay, popularly known as increment or upward movement in the hierarchy i.e. promotion. Traditionally PA is a straightforward process where the manager can only review the performance of his subordinates annually. However, in order to meet the increased expectations of employers, customer demand and better output, PA plays a crucial role for which it has become a part of a wider HRM strategy called performance management (PM). Traditional PA has some limitations that can be minimised by following multi-rater PA system. 360-degree feedback and balanced scorecard are playing important role as recent innovations in the PM system. These two tools are being widely used in both public and private organisations of developed countries like UK, USA, Germany, France; even in the public sectors in Bangladesh, Non-Government Organisations (NGOs) in particular. This paper aims at discussing the traditional PA system experienced with several problems which the new innovations such as 360 degree feedback and balanced scorecard have attempted to overcome.
Build efficient data flow and machine learning programs with this flexible, multi-functional open-source cluster-computing framework Key Features Master the art of real-time big data processing and machine learning Explore a wide range of use-cases to analyze large data Discover ways to optimize your work by using many features of Spark 2.x and Scala Book Description Apache Spark is an in-memory, cluster-based data processing system that provides a wide range of functionalities such as big data processing, analytics, machine learning, and more. With this Learning Path, you can take your knowledge of Apache Spark to the next level by learning how to expand Spark's functionality and building your own data flow and machine learning programs on this platform. You will work with the different modules in Apache Spark, such as interactive querying with Spark SQL, using DataFrames and datasets, implementing streaming analytics with Spark Streaming, and applying machine learning and deep learning techniques on Spark using MLlib and various external tools. By the end of this elaborately designed Learning Path, you will have all the knowledge you need to master Apache Spark, and build your own big data processing and analytics pipeline quickly and without any hassle. This Learning Path includes content from the following Packt products: Mastering Apache Spark 2.x by Romeo Kienzler Scala and Spark for Big Data Analytics by Md. Rezaul Karim, Sridhar Alla Apache Spark 2.x Machine Learning Cookbook by Siamak Amirghodsi, Meenakshi Rajendran, Broderick Hall, Shuen MeiCookbook What you will learn Get to grips with all the features of Apache Spark 2.x Perform highly optimized real-time big data processing Use ML and DL techniques with Spark MLlib and third-party tools Analyze structured and unstructured data using SparkSQL and GraphX Understand tuning, debugging, and monitoring of big data applications Build scalable and fault-tolerant streaming applications Develop scalable recommendation engines Who this book is for If you are an intermediate-level Spark developer looking to master the advanced capabilities and use-cases of Apache Spark 2.x, this Learning Path is ideal for you. Big data professionals who want to learn how to integrate and use the features of Apache Spark and build a strong big data pipeline will also find this Learning Path useful. To grasp the concepts explained in this Learning Path, you must know the fundamentals of Apache Spark and Scala.
Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of TensorFlow. Key Features Learn how to implement advanced techniques in deep learning with Google's brainchild, TensorFlow Explore deep neural networks and layers of data abstraction with the help of this comprehensive guide Gain real-world contextualization through some deep learning problems concerning research and application Book Description Deep learning is a branch of machine learning algorithms based on learning multiple levels of abstraction. Neural networks, which are at the core of deep learning, are being used in predictive analytics, computer vision, natural language processing, time series forecasting, and to perform a myriad of other complex tasks. This book is conceived for developers, data analysts, machine learning practitioners and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries. Throughout the book, you’ll learn how to develop deep learning applications for machine learning systems using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Factorization Machines. Discover how to attain deep learning programming on GPU in a distributed way. You'll come away with an in-depth knowledge of machine learning techniques and the skills to apply them to real-world projects. What you will learn Apply deep machine intelligence and GPU computing with TensorFlow Access public datasets and use TensorFlow to load, process, and transform the data Discover how to use the high-level TensorFlow API to build more powerful applications Use deep learning for scalable object detection and mobile computing Train machines quickly to learn from data by exploring reinforcement learning techniques Explore active areas of deep learning research and applications Who this book is for The book is for people interested in machine learning and machine intelligence. A rudimentary level of programming in one language is assumed, as is a basic familiarity with computer science techniques and technologies, including a basic awareness of computer hardware and algorithms. Some competence in mathematics is needed to the level of elementary linear algebra and calculus.
Harness the power of Scala to program Spark and analyze tonnes of data in the blink of an eye! About This Book Learn Scala's sophisticated type system that combines Functional Programming and object-oriented concepts Work on a wide array of applications, from simple batch jobs to stream processing and machine learning Explore the most common as well as some complex use-cases to perform large-scale data analysis with Spark Who This Book Is For Anyone who wishes to learn how to perform data analysis by harnessing the power of Spark will find this book extremely useful. No knowledge of Spark or Scala is assumed, although prior programming experience (especially with other JVM languages) will be useful to pick up concepts quicker. What You Will Learn Understand object-oriented & functional programming concepts of Scala In-depth understanding of Scala collection APIs Work with RDD and DataFrame to learn Spark's core abstractions Analysing structured and unstructured data using SparkSQL and GraphX Scalable and fault-tolerant streaming application development using Spark structured streaming Learn machine-learning best practices for classification, regression, dimensionality reduction, and recommendation system to build predictive models with widely used algorithms in Spark MLlib & ML Build clustering models to cluster a vast amount of data Understand tuning, debugging, and monitoring Spark applications Deploy Spark applications on real clusters in Standalone, Mesos, and YARN In Detail Scala has been observing wide adoption over the past few years, especially in the field of data science and analytics. Spark, built on Scala, has gained a lot of recognition and is being used widely in productions. Thus, if you want to leverage the power of Scala and Spark to make sense of big data, this book is for you. The first part introduces you to Scala, helping you understand the object-oriented and functional programming concepts needed for Spark application development. It then moves on to Spark to cover the basic abstractions using RDD and DataFrame. This will help you develop scalable and fault-tolerant streaming applications by analyzing structured and unstructured data using SparkSQL, GraphX, and Spark structured streaming. Finally, the book moves on to some advanced topics, such as monitoring, configuration, debugging, testing, and deployment. You will also learn how to develop Spark applications using SparkR and PySpark APIs, interactive data analytics using Zeppelin, and in-memory data processing with Alluxio. By the end of this book, you will have a thorough understanding of Spark, and you will be able to perform full-stack data analytics with a feel that no amount of data is too big. Style and approach Filled with practical examples and use cases, this book will hot only help you get up and running with Spark, but will also take you farther down the road to becoming a data scientist.
Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of this comprehensive TensorFlow guide About This Book Learn how to implement advanced techniques in deep learning with Google's brainchild, TensorFlow Explore deep neural networks and layers of data abstraction with the help of this comprehensive guide Real-world contextualization through some deep learning problems concerning research and application Who This Book Is For The book is intended for a general audience of people interested in machine learning and machine intelligence. A rudimentary level of programming in one language is assumed, as is a basic familiarity with computer science techniques and technologies, including a basic awareness of computer hardware and algorithms. Some competence in mathematics is needed to the level of elementary linear algebra and calculus. What You Will Learn Learn about machine learning landscapes along with the historical development and progress of deep learning Learn about deep machine intelligence and GPU computing with the latest TensorFlow 1.x Access public datasets and utilize them using TensorFlow to load, process, and transform data Use TensorFlow on real-world datasets, including images, text, and more Learn how to evaluate the performance of your deep learning models Using deep learning for scalable object detection and mobile computing Train machines quickly to learn from data by exploring reinforcement learning techniques Explore active areas of deep learning research and applications In Detail Deep learning is the step that comes after machine learning, and has more advanced implementations. Machine learning is not just for academics anymore, but is becoming a mainstream practice through wide adoption, and deep learning has taken the front seat. As a data scientist, if you want to explore data abstraction layers, this book will be your guide. This book shows how this can be exploited in the real world with complex raw data using TensorFlow 1.x. Throughout the book, you'll learn how to implement deep learning algorithms for machine learning systems and integrate them into your product offerings, including search, image recognition, and language processing. Additionally, you'll learn how to analyze and improve the performance of deep learning models. This can be done by comparing algorithms against benchmarks, along with machine intelligence, to learn from the information and determine ideal behaviors within a specific context. After finishing the book, you will be familiar with machine learning techniques, in particular the use of TensorFlow for deep learning, and will be ready to apply your knowledge to research or commercial projects. Style and approach This step-by-step guide will explore common, and not so common, deep neural networks and show how these can be exploited in the real world with complex raw data. With the help of practical examples, you will learn how to implement different types of neural nets to build smart applications related to text, speech, and image data processing.
Build efficient data flow and machine learning programs with this flexible, multi-functional open-source cluster-computing framework Key FeaturesMaster the art of real-time big data processing and machine learning Explore a wide range of use-cases to analyze large data Discover ways to optimize your work by using many features of Spark 2.x and ScalaBook Description Apache Spark is an in-memory, cluster-based data processing system that provides a wide range of functionalities such as big data processing, analytics, machine learning, and more. With this Learning Path, you can take your knowledge of Apache Spark to the next level by learning how to expand Spark's functionality and building your own data flow and machine learning programs on this platform. You will work with the different modules in Apache Spark, such as interactive querying with Spark SQL, using DataFrames and datasets, implementing streaming analytics with Spark Streaming, and applying machine learning and deep learning techniques on Spark using MLlib and various external tools. By the end of this elaborately designed Learning Path, you will have all the knowledge you need to master Apache Spark, and build your own big data processing and analytics pipeline quickly and without any hassle. This Learning Path includes content from the following Packt products: Mastering Apache Spark 2.x by Romeo KienzlerScala and Spark for Big Data Analytics by Md. Rezaul Karim, Sridhar AllaApache Spark 2.x Machine Learning Cookbook by Siamak Amirghodsi, Meenakshi Rajendran, Broderick Hall, Shuen MeiCookbookWhat you will learnGet to grips with all the features of Apache Spark 2.xPerform highly optimized real-time big data processing Use ML and DL techniques with Spark MLlib and third-party toolsAnalyze structured and unstructured data using SparkSQL and GraphXUnderstand tuning, debugging, and monitoring of big data applications Build scalable and fault-tolerant streaming applications Develop scalable recommendation enginesWho this book is for If you are an intermediate-level Spark developer looking to master the advanced capabilities and use-cases of Apache Spark 2.x, this Learning Path is ideal for you. Big data professionals who want to learn how to integrate and use the features of Apache Spark and build a strong big data pipeline will also find this Learning Path useful. To grasp the concepts explained in this Learning Path, you must know the fundamentals of Apache Spark and Scala.
Research Paper (postgraduate) from the year 2013 in the subject Sociology - Work, Education, Organisation, National Institute of Development Administration (Graduate School of Public Administration), course: PhD, language: English, abstract: The effects of globalisation on gendered division of labour, global market strategies, structural adjustment programmes, use of information technology and changing work organisation, working conditions are changed in different formats and there has been an increase of female participation in employment This change becomes more significant especially for the involvement of female workers at the workplace with a great responsibility at home for family activities. Like their male counterparts women are also earners for their families. However, they have to maintain their family activities such as caring young children, looking after ageing and disabled people that seems women have to do more than their men peers. Having realized this fact, family-friendly policies have been enacted and practised in the organization on the basis of recommendations of policy makers, researchers with an intention to utilize the women capital and play an important role in uplifting living conditions of women. These FFPs serve as the balancing between work and family live. These issues are considered as the benefit for working women with or without children who enjoy the advantage to utilise their time for family responsibilities As these are the concerns, the issue of quality of life has become a new aspect for the increase of female participation in labour force . Bangladesh is waved by the globalisation, internationalisation, pressure of donor agencies and recent change in the growth of female employment. This research has been framed to examine the causal relations between the family friendly policies and working life of women in the public sector training organizations.The population size comprises of all working women of these organizations. The study population (502) is the aggregation of elements from which the sample is selected. The Sample size selected from two strata i.e. managerial level and non-managerial (support staff) level employees are 156 and 249 respectively that calculated using the formula. This is a quantitative research where Family-friendly policies are the independent variables and working life of working women is the dependent variable. Hypotheses taken relating to the independent and dependent variables will be tested using the various statistical methods. The unit of analysis of this study is working women employed in the public sector training organizations. After getting data from the respondents, data will be analyzed using SPSS.
Essay in the subject Agrarian Studies, , language: English, abstract: The key objective of the study is to examine the use of mobile phones by the farmers how smartly they can utilize for their agricularal development. The study is designed to achieve specific objectives, which are: Exploring the use of cell phone pertaining to access and receipt of agricultural information. Investigating the understanding the technology and participation of the farmers in receiving mobile phone based services. Examining the obstacles that the famers face in terms access and getting information related to agriculture products, process. Bangladesh is predominantly an agrarian country. Despite several challenges, the country has achieved self-sufficiency in cereal food production. It is already evident that e-agriculture especially the expansion of mobile phone has tremendously boosted up the agricultural production in Bangladesh. Couples of vibrant mobile based agro advisory initiatives have also been evolved in last decade. The present study has been made to find out the status of the farmers in understanding and participation in access to agricultural information using mobile phone. Ten farmers were randomly selected from Dhamrai upazila of Dhaka district and Manikganj Sadar upazilla of Manikganj district. Semi-structured questionary-based survey, FGD was done.
Essay from the year 2013 in the subject Economics - Case Scenarios, grade: "none", , course: PhD, language: English, abstract: Abstract: To reduce inequality in income distribution and reduce the poverty social welfare spending in Thailand particularly on education and health services is regarded as one of the effective instruments. Policy makers agree that public subsidies on education and health produce positive externalities and have spill over effect in the society. This research is designed to analyse the effects of public spending of education and health on income distribution which examine the pre-expenditure and post-expenditure income distribution in Thailand. It follows the benefit incidence analysis (BIA) that is a method of computing the distribution of public expenditure across different demographic groups, such as women and men. The procedure involves allocating per unit public subsidies (for example, expenditure per student for the education sector) according to individual utilization rates of public services. This paper aims at examine who are the real beneficiaries from the government expenditure. The study uses the quantitative method where data are used of 2010. From the benefit incidence analysis on the public expenditure on education it seems that education system is pro-poor and health care system are not pro-poor rather pro-rich. The poor people can be benefited more from the primary and secondary education and less benefited from the tertiary education. However, overall expenditure on education is favourable to the poor which proves from the income share of household. In this perspective, The Thai government should emphasize on higher education for poor by providing special loan created only for them and universities should also be adopted the policy so that poor income class people can access the opportunity. Government can increase the charge and fees for private higher education where normally rich households send their children. By doing so, government can earn more and spend for poor people. Regarding the healthcare system, Thai government should emphasize on preventive care than curative from which the whole nation will be benefitted. The programmes should be continued and more expenditure should be added to this. Government should charge tax on private healthcare system that will help collect more money and invest for the poor people. Since rich tends to go to the private hospitals, government will take money from the rich and spend for poor people. This process will minimize the income inequality.
Accomplish the power of data in your business by building advanced predictive modelling applications with Tensorflow.About This Book* A quick guide to gain hands-on experience with deep learning in different domains such as digit/image classification, and texts* Build your own smart, predictive models with TensorFlow using easy-to-follow approach mentioned in the book* Understand deep learning and predictive analytics along with its challenges and best practicesWho This Book Is ForThis book is intended for anyone who wants to build predictive models with the power of TensorFlow from scratch. If you want to build your own extensive applications which work, and can predict smart decisions in the future then this book is what you need!What You Will Learn* Get a solid and theoretical understanding of linear algebra, statistics, and probability for predictive modeling* Develop predictive models using classification, regression, and clustering algorithms* Develop predictive models for NLP* Learn how to use reinforcement learning for predictive analytics* Factorization Machines for advanced recommendation systems* Get a hands-on understanding of deep learning architectures for advanced predictive analytics* Learn how to use deep Neural Networks for predictive analytics* See how to use recurrent Neural Networks for predictive analytics* Convolutional Neural Networks for emotion recognition, image classification, and sentiment analysisIn DetailPredictive analytics discovers hidden patterns from structured and unstructured data for automated decision-making in business intelligence.This book will help you build, tune, and deploy predictive models with TensorFlow in three main sections. The first section covers linear algebra, statistics, and probability theory for predictive modeling.The second section covers developing predictive models via supervised (classification and regression) and unsupervised (clustering) algorithms. It then explains how to develop predictive models for NLP and covers reinforcement learning algorithms. Lastly, this section covers developing a factorization machines-based recommendation system.The third section covers deep learning architectures for advanced predictive analytics, including deep neural networks and recurrent neural networks for high-dimensional and sequence data. Finally, convolutional neural networks are used for predictive modeling for emotion recognition, image classification, and sentiment analysis.Style and approachTensorFlow, a popular library for machine learning, embraces the innovation and community-engagement of open source, but has the support, guidance, and stability of a large corporation.
This book presents and standardizes statistical models and methods that can be directly applied to both reliability and survival analysis. These two types of analysis are widely used in many fields, including engineering, management, medicine, actuarial science, the environmental sciences, and the life sciences. Though there are a number of books on reliability analysis and a handful on survival analysis, there are virtually no books on both topics and their overlapping concepts. Offering an essential textbook, this book will benefit students, researchers, and practitioners in reliability and survival analysis, reliability engineering, biostatistics, and the biomedical sciences.
In emerging and global economies, e-commerce and e-business have become increasingly necessary components of business strategy and strong catalysts for economic development. Strategic and Pragmatic E-Business: Implications for Future Business Practices disseminates information on the new practices and techniques in e-business and promotes a better understanding of contemporary issues and solutions to existing and emerging challenges. Making prospective audiences aware of concurrent business needs, models, trends, methods, and techniques, this books targets: e-commerce vendors, business managers, entrepreneurs, software industries, the digital media world, and online merchants who wish to build strong consumer brands.
Warranty Data Collection and Analysis deals with warranty data collection and analysis and the problems associated with these activities. The book is a both a research monograph and a handbook for practitioners. As a research monograph, it unifies the literature on warranty data collection and analysis, and presents the important results in an integrated manner. In the process, it highlights topics that require further research. As a handbook, it provides the essential methodology needed by practitioners involved with warranty data collection and analysis, along with extensive references to further results. Models and techniques needed for proper and effective analysis of data are included, together with guidelines for their use in warranty management, product improvement, and new product development. Warranty Data Collection and Analysis will be of interest to researchers (engineers and statisticians) and practitioners (engineers, applied statisticians, and managers) involved with product warranty and reliability. It is also suitable for use as a reference text for graduate-level reliability programs in engineering, applied statistics, operations research, and management.
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