James D. Miller – författare

The authorship of the Pastoral letters has been a matter of intense scholarly debate for almost two hundred years. The letters clearly purport to be written by Paul, but perceived differences in the literary style, vocabulary and theology of the Pastorals when compared with that of the genuine Pauline letters suggests that this was not so. The arguments have centred primarily on the question of whether Paul or a disciple of Paul - a gifted pseudonymist - composed these letters. It is the 'either/or' nature of the debate that is brought into serious question in this book. Dr Miller argues that the Pastorals reflect a compositional history that was commonplace throughout the ancient Near East. He takes the reader on a wide-ranging tour of biblical and extra-biblical sources, examining their literary histories, and arguing that the Pastorals are composite documents, not unlike many Jewish and early Christian works.

The authorship of the Pastoral letters has been a matter of intense scholarly debate for almost two hundred years. The letters clearly purport to be written by Paul, but perceived differences in the literary style, vocabulary and theology of the Pastorals when compared with that of the genuine Pauline letters suggests that this was not so. The arguments have centred primarily on the question of whether Paul or a disciple of Paul - a gifted pseudonymist - composed these letters. It is the 'either/or' nature of the debate that is brought into serious question in this book. Dr Miller argues that the Pastorals reflect a compositional history that was commonplace throughout the ancient Near East. He takes the reader on a wide-ranging tour of biblical and extra-biblical sources, examining their literary histories, and arguing that the Pastorals are composite documents, not unlike many Jewish and early Christian works.

Master the craft of predictive modeling in R by developing strategy, intuition, and a solid foundation in essential conceptsAbout This Book• Grasping the major methods of predictive modeling and moving beyond black box thinking to a deeper level of understanding• Leveraging the flexibility and modularity of R to experiment with a range of different techniques and data types• Packed with practical advice and tips explaining important concepts and best practices to help you understand quickly and easilyWho This Book Is ForAlthough budding data scientists, predictive modelers, or quantitative analysts with only basic exposure to R and statistics will find this book to be useful, the experienced data scientist professional wishing to attain master level status , will also find this book extremely valuable.. This book assumes familiarity with the fundamentals of R, such as the main data types, simple functions, and how to move data around. Although no prior experience with machine learning or predictive modeling is required, there are some advanced topics provided that will require more than novice exposure.What You Will Learn• Master the steps involved in the predictive modeling process• Grow your expertise in using R and its diverse range of packages• Learn how to classify predictive models and distinguish which models are suitable for a particular problem• Understand steps for tidying data and improving the performing metrics• Recognize the assumptions, strengths, and weaknesses of a predictive model• Understand how and why each predictive model works in R• Select appropriate metrics to assess the performance of different types of predictive model• Explore word embedding and recurrent neural networks in R• Train models in R that can work on very large datasetsIn DetailR offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions. With its constantly growing community and plethora of packages, R offers the functionality to deal with a truly vast array of problems.The book begins with a dedicated chapter on the language of models and the predictive modeling process. You will understand the learning curve and the process of tidying data. Each subsequent chapter tackles a particular type of model, such as neural networks, and focuses on the three important questions of how the model works, how to use R to train it, and how to measure and assess its performance using real-world datasets. How do you train models that can handle really large datasets? This book will also show you just that. Finally, you will tackle the really important topic of deep learning by implementing applications on word embedding and recurrent neural networks.By the end of this book, you will have explored and tested the most popular modeling techniques in use on real- world datasets and mastered a diverse range of techniques in predictive analytics using R.Style and approachThis book takes a step-by-step approach in explaining the intermediate to advanced concepts in predictive analytics. Every concept is explained in depth, supplemented with practical examples applicable in a real-world setting.

Get your statistics basics right before diving into the world of data scienceAbout This Book• No need to take a degree in statistics, read this book and get a strong statistics base for data science and real-world programs;• Implement statistics in data science tasks such as data cleaning, mining, and analysis• Learn all about probability, statistics, numerical computations, and more with the help of R programsWho This Book Is ForThis book is intended for those developers who are willing to enter the field of data science and are looking for concise information of statistics with the help of insightful programs and simple explanation. Some basic hands on R will be useful.What You Will Learn• Analyze the transition from a data developer to a data scientist mindset• Get acquainted with the R programs and the logic used for statistical computations• Understand mathematical concepts such as variance, standard deviation, probability, matrix calculations, and more• Learn to implement statistics in data science tasks such as data cleaning, mining, and analysis• Learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks• Get comfortable with performing various statistical computations for data science programmaticallyIn DetailData science is an ever-evolving field, which is growing in popularity at an exponential rate. Data science includes techniques and theories extracted from the fields of statistics; computer science, and, most importantly, machine learning, databases, data visualization, and so on.This book takes you through an entire journey of statistics, from knowing very little to becoming comfortable in using various statistical methods for data science tasks. It starts off with simple statistics and then move on to statistical methods that are used in data science algorithms. The R programs for statistical computation are clearly explained along with logic. You will come across various mathematical concepts, such as variance, standard deviation, probability, matrix calculations, and more. You will learn only what is required to implement statistics in data science tasks such as data cleaning, mining, and analysis. You will learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks.By the end of the book, you will be comfortable with performing various statistical computations for data science programmatically.Style and approachStep by step comprehensive guide with real world examples

Incorporate intelligence to your data-driven business insights and high accuracy business solutionsKey FeaturesExplore IBM Watson capabilities such as Natural Language Processing (NLP) and machine learningBuild projects to adopt IBM Watson across retail, banking, and healthcareLearn forecasting, anomaly detection, and pattern recognition with ML techniquesBook DescriptionIBM Watson provides fast, intelligent insight in ways that the human brain simply can't match. Through eight varied projects, this book will help you explore the computing and analytical capabilities of IBM Watson.The book begins by refreshing your knowledge of IBM Watson's basic data preparation capabilities, such as adding and exploring data to prepare it for being applied to models. The projects covered in this book can be developed for different industries, including banking, healthcare, media, and security. These projects will enable you to develop an AI mindset and guide you in developing smart data-driven projects, including automating supply chains, analyzing sentiment in social media datasets, and developing personalized recommendations.By the end of this book, you'll have learned how to develop solutions for process automation, and you'll be able to make better data-driven decisions to deliver an excellent customer experience.What you will learnBuild a smart dialog system with cognitive assistance solutionsDesign a text categorization model and perform sentiment analysis on social media datasetsDevelop a pattern recognition application and identify data irregularities smartlyAnalyze trip logs from a driving services company to determine profitProvide insights into an organization s supply chain data and processesCreate personalized recommendations for retail chains and outletsTest forecasting effectiveness for better sales prediction strategiesWho this book is forThis book is for data scientists, AI engineers, NLP engineers, machine learning engineers, and data analysts who wish to build next-generation analytics applications. Basic familiarity with cognitive computing and sound knowledge of any programming language is all you need to understand the projects covered in this book.

Learn how to build complete machine learning systems with IBM Cloud and Watson Machine learning servicesKey FeaturesImplement data science and machine learning techniques to draw insights from real-world dataUnderstand what IBM Cloud platform can help you to implement cognitive insights within applicationsUnderstand the role of data representation and feature extraction in any machine learning systemBook DescriptionIBM Cloud is a collection of cloud computing services for data analytics using machine learning and artificial intelligence (AI). This book is a complete guide to help you become well versed with machine learning on the IBM Cloud using Python. Hands-On Machine Learning with IBM Watson starts with supervised and unsupervised machine learning concepts, in addition to providing you with an overview of IBM Cloud and Watson Machine Learning. You'll gain insights into running various techniques, such as K-means clustering, K-nearest neighbor (KNN), and time series prediction in IBM Cloud with real-world examples. The book will then help you delve into creating a Spark pipeline in Watson Studio. You will also be guided through deep learning and neural network principles on the IBM Cloud using TensorFlow. With the help of NLP techniques, you can then brush up on building a chatbot. In later chapters, you will cover three powerful case studies, including the facial expression classification platform, the automated classification of lithofacies, and the multi-biometric identity authentication platform, helping you to become well versed with these methodologies.By the end of this book, you will be ready to build efficient machine learning solutions on the IBM Cloud and draw insights from the data at hand using real-world examples.What you will learnUnderstand key characteristics of IBM machine learning servicesRun supervised and unsupervised techniques in the cloudUnderstand how to create a Spark pipeline in Watson StudioImplement deep learning and neural networks on the IBM Cloud with TensorFlowCreate a complete, cloud-based facial expression classification solutionUse biometric traits to build a cloud-based human identification systemWho this book is forThis beginner-level book is for data scientists and machine learning engineers who want to get started with IBM Cloud and its machine learning services using practical examples. Basic knowledge of Python and some understanding of machine learning will be useful.