John Winn – författare

SELECTIONS: A Journey Toward Spiritual FormationThe opening words of the Prologue are as good a beginning toward a description of the book as any:What began as a contemplative practice soon became a time of self-examination, and then an ongoing reading of the New Testament, followed by an aroused intellectual curiosity that led to research into scriptural exegesis, and finally, after years of repetition and reflection, to a satisfying experience of internalization. Somewhere along the way I realized I was working on my own personal spiritual formation. This is how my alternative New Testament Lectionary came into being.My uncommon lectionary is an invitation to a spiritual pilgrimage through salient selections of New Testament passages. For those involved in or interested in the Christian Movement there is no better place to dig deeper. The New Testament text is provided so one does not have to fumble around to find ones own copy.After describing the evolution of the process, the book is divided into the seasons of the Christian Year. Each week correlates a Gospel Reading and an Epistle Reading. There is background material for each section, setting the stage for the specific season. The reader is guided through the reading in a lectio divina style, with variations to keep it from becoming too repetitious. Unique to the book are some gentle challenges in each weeks reading to help the reader press beneath the surface. These vary with each season, ranging from an invitation to record several I Believe statements about a particular passage to creating three handwritten, free-flowing Lenten Pages. During Holy Week one may be asked to practice one hour of Sacred Silence. Pentecost challenges the reader to compose a Haiku based on the passages for the week. Missiontide presses for an essay of no less than three, no more than five sentences on each passage relative to the question, What now is expected of me.These gentle challenges are designed to lead one to deeper reflection and clearer focus on the lectionary passages for a given week. They help us to activate our souls contemplative nature. They also encourage us to allow the key words in a passage to be formed into a personal prayer. I believe that serious reflection, focus, contemplation, and prayer can draw us along a path toward spiritual formation.The Seasons of the Christian Year have a mystical correlation to the seasons of our own lives. To my mind, this book has an appeal to that general audience that wants to discover the deeper, more progressive aspects of the Christian Faith. For many in the general audience, SELECTIONS: A Journey Toward Spiritual Formation will be simply a book of daily devotions. I believe, too, that churches will find it helpful and effective in retreats, small groups, and class sessions. Many of my colleagues in ministry have expressed an interest in an alternative lectionary. They, too, would find this book very useful. I have tested it in all these ways with very positive responses.

Today, machine learning is being applied to a growing variety of problems in a bewildering variety of domains. A fundamental challenge when using machine learning is connecting the abstract mathematics of a machine learning technique to a concrete, real world problem. This book tackles this challenge through model-based machine learning which focuses on understanding the assumptions encoded in a machine learning system and their corresponding impact on the behaviour of the system.The key ideas of model-based machine learning are introduced through a series of case studies involving real-world applications. Case studies play a central role because it is only in the context of applications that it makes sense to discuss modelling assumptions. Each chapter introduces one case study and works through step-by-step to solve it using a model-based approach. The aim is not just to explain machine learning methods, but also showcase how to create, debug, and evolve them to solve a problem.Features:Explores the assumptions being made by machine learning systems and the effect these assumptions have when the system is applied to concrete problems.Explains machine learning concepts as they arise in real-world case studies.Shows how to diagnose, understand and address problems with machine learning systems.Full source code available, allowing models and results to be reproduced and explored.Includes optional deep-dive sections with more mathematical details on inference algorithms for the interested reader.

Today, machine learning is being applied to a growing variety of problems in a bewildering variety of domains. A fundamental challenge when using machine learning is connecting the abstract mathematics of a machine learning technique to a concrete, real world problem. This book tackles this challenge through model-based machine learning which focuses on understanding the assumptions encoded in a machine learning system and their corresponding impact on the behaviour of the system.

The key ideas of model-based machine learning are introduced through a series of case studies involving real-world applications. Case studies play a central role because it is only in the context of applications that it makes sense to discuss modelling assumptions. Each chapter introduces one case study and works through step-by-step to solve it using a model-based approach. The aim is not just to explain machine learning methods, but also showcase how to create, debug, and evolve them to solve a problem.

Features:

Explores the assumptions being made by machine learning systems and the effect these assumptions have when the system is applied to concrete problems. Explains machine learning concepts as they arise in real-world case studies. Shows how to diagnose, understand and address problems with machine learning systems. Full source code available, allowing models and results to be reproduced and explored. Includes optional deep-dive sections with more mathematical details on inference algorithms for the interested reader.

Today, machine learning is being applied to a growing variety of problems in a bewildering variety of domains. A fundamental challenge when using machine learning is connecting the abstract mathematics of a machine learning technique to a concrete, real world problem. This book tackles this challenge through model-based machine learning which focuses on understanding the assumptions encoded in a machine learning system and their corresponding impact on the behaviour of the system.

The key ideas of model-based machine learning are introduced through a series of case studies involving real-world applications. Case studies play a central role because it is only in the context of applications that it makes sense to discuss modelling assumptions. Each chapter introduces one case study and works through step-by-step to solve it using a model-based approach. The aim is not just to explain machine learning methods, but also showcase how to create, debug, and evolve them to solve a problem.

Features:

Explores the assumptions being made by machine learning systems and the effect these assumptions have when the system is applied to concrete problems. Explains machine learning concepts as they arise in real-world case studies. Shows how to diagnose, understand and address problems with machine learning systems. Full source code available, allowing models and results to be reproduced and explored. Includes optional deep-dive sections with more mathematical details on inference algorithms for the interested reader.