Wydawca: K-i-s-publishing
Cecil 'Gary' Rupp
Scaled Scrum and Lean-Agile practices provide essential strategies to address large and complex product development challenges not addressed in traditional Scrum. This Scrum/ Lean-Agile handbook provides a comprehensive review and analysis of industry-proven scaling strategies that enable business agility on an enterprise scale. Free of marketing hype or vendor bias, this book helps you decide which practices best fit your situation.You'll start with an introduction to Scrum as a lightweight software development framework and then explore common approaches to scaling it for more complex development scenarios. The book will then guide you through systems theory, lean development, and the application of holistic thinking to more complex software and system development activities. Throughout, you'll learn how to support multiple teams working in collaboration to develop large and complex products and explore how to manage cross-team integration, dependency, and synchronization issues. Later, you'll learn how to improve enterprise operational efficiency across value creation and value delivery activities, before discovering how to align product portfolio investments with corporate strategies.By the end of this Scrum book, you and your product teams will be able to get the most value out of Agile at scale, even in complex cyber-physical system development environments.
Hannah Fielding
Eine unvergessliche Leidenschaft, die sich im Herzen Afrikas entspinnt. Eine zerbrechliche Liebe, die von Geheimnissen und Betrug gefährdet wird. Coral Sinclair, eine schöne, aber naive junge Fotografin, erfährt innerhalb weniger Tage nach der Absage ihrer Hochzeit, dass sie ihren Vater verloren hat. Sie verlässt ihr Leben in England und reist nach Kenia, um ihr Erbe anzutreten Mpingo, die Plantage, auf der sie ihre Kindheit verbrachte. Auf der Schiffsreise trifft Coral einen charismatischen Fremden, dessen mysteriöse Anziehungskraft sie bis ins Mark erschüttert. Später findet sie seine Identität heraus und wird gewarnt, diesem Mann nicht zu vertrauen. Rafe de Monfort, Besitzer eines Nachtclubs und der benachbarten Plantage, ist nicht nur ein berüchtigter Frauenheld seine Affäre mit Corals Stiefmutter hat möglicherweise zum Tod ihres Vaters beigetragen. Zumindest besagen dies die Gerüchte. Während Coral der unbestreitbaren Chemie zwischen ihr und Rafe verfällt, blüht in der exotischen, gefährlichen Wildnis Afrikas eine vorsichtige Romanze auf. Aber als Coral sich mit Rafes Vergangenheit befasst, hinterfragt sie seine wahren Motive. Ist dieser berütigte Mann nur hinter ihrem Erbe her? Oder stecken Rafes geheime Qualen hinter jeder Handlung und machen ihn verletzlicher, als Coral es sich jemals vorstellen könnte?
Claus Führer, Claus Fuhrer, Jan Erik Solem,...
Python can be used for more than just general-purpose programming. It is a free, open source language and environment that has tremendous potential for use within the domain of scientific computing. This book presents Python in tight connection with mathematical applications and demonstrates how to use various concepts in Python for computing purposes, including examples with the latest version of Python 3. Python is an effective tool to use when coupling scientific computing and mathematics and this book will teach you how to use it for linear algebra, arrays, plotting, iterating, functions, polynomials, and much more.
Claus Führer, Claus Fuhrer, Jan Erik Solem,...
Python has tremendous potential within the scientific computing domain. This updated edition of Scientific Computing with Python features new chapters on graphical user interfaces, efficient data processing, and parallel computing to help you perform mathematical and scientific computing efficiently using Python.This book will help you to explore new Python syntax features and create different models using scientific computing principles. The book presents Python alongside mathematical applications and demonstrates how to apply Python concepts in computing with the help of examples involving Python 3.8. You'll use pandas for basic data analysis to understand the modern needs of scientific computing, and cover data module improvements and built-in features. You'll also explore numerical computation modules such as NumPy and SciPy, which enable fast access to highly efficient numerical algorithms. By learning to use the plotting module Matplotlib, you will be able to represent your computational results in talks and publications. A special chapter is devoted to SymPy, a tool for bridging symbolic and numerical computations.By the end of this Python book, you'll have gained a solid understanding of task automation and how to implement and test mathematical algorithms within the realm of scientific computing.
Vytautas Jancauskas
Scala is a statically typed, Java Virtual Machine (JVM)-based language with strong support for functional programming. There exist libraries for Scala that cover a range of common scientific computing tasks – from linear algebra and numerical algorithms to convenient and safe parallelization to powerful plotting facilities. Learning to use these to perform common scientific tasks will allow you to write programs that are both fast and easy to write and maintain.We will start by discussing the advantages of using Scala over other scientific computing platforms. You will discover Scala packages that provide the functionality you have come to expect when writing scientific software. We will explore using Scala's Breeze library for linear algebra, optimization, and signal processing. We will then proceed to the Saddle library for data analysis. If you have experience in R or with Python's popular pandas library you will learn how to translate those skills to Saddle. If you are new to data analysis, you will learn basic concepts of Saddle as well. Well will explore the numerical computing environment called ScalaLab. It comes bundled with a lot of scientific software readily available. We will use it for interactive computing, data analysis, and visualization. In the following chapters, we will explore using Scala's powerful parallel collections for safe and convenient parallel programming. Topics such as the Akka concurrency framework will be covered. Finally, you will learn about multivariate data visualization and how to produce professional-looking plots in Scala easily. After reading the book, you should have more than enough information on how to start using Scala as your scientific computing platform
Julian Avila, Trent Hauck
Python is quickly becoming the go-to language for analysts and data scientists due to its simplicity and flexibility, and within the Python data space, scikit-learn is the unequivocal choice for machine learning. This book includes walk throughs and solutions to the common as well as the not-so-common problems in machine learning, and how scikit-learn can be leveraged to perform various machine learning tasks effectively.The second edition begins with taking you through recipes on evaluating the statistical properties of data and generates synthetic data for machine learning modelling. As you progress through the chapters, you will comes across recipes that will teach you to implement techniques like data pre-processing, linear regression, logistic regression, K-NN, Naïve Bayes, classification, decision trees, Ensembles and much more. Furthermore, you’ll learn to optimize your models with multi-class classification, cross validation, model evaluation and dive deeper in to implementing deep learning with scikit-learn. Along with covering the enhanced features on model section, API and new features like classifiers, regressors and estimators the book also contains recipes on evaluating and fine-tuning the performance of your model. By the end of this book, you will have explored plethora of features offered by scikit-learn for Python to solve any machine learning problem you come across.
John Sukup
Trusted by data scientists, ML engineers, and software developers alike, scikit-learn offers a versatile, user-friendly framework for implementing a wide range of ML algorithms, enabling the efficient development and deployment of predictive models in real-world applications. This third edition of scikit-learn Cookbook will help you master ML with real-world examples and scikit-learn 1.5 features.This updated edition takes you on a journey from understanding the fundamentals of ML and data preprocessing, through implementing advanced algorithms and techniques, to deploying and optimizing ML models in production. Along the way, you’ll explore practical, step-by-step recipes that cover everything from feature engineering and model selection to hyperparameter tuning and model evaluation, all using scikit-learn.By the end of this book, you’ll have gained the knowledge and skills needed to confidently build, evaluate, and deploy sophisticated ML models using scikit-learn, ready to tackle a wide range of data-driven challenges.*Email sign-up and proof of purchase required
Guillermo Moncecchi, Raul G Tompson, Trent Hauck,...
Machine learning, the art of creating applications that learn from experience and data, has been around for many years. Python is quickly becoming the go-to language for analysts and data scientists due to its simplicity and flexibility; moreover, within the Python data space, scikit-learn is the unequivocal choice for machine learning. The course combines an introduction to some of the main concepts and methods in machine learning with practical, hands-on examples of real-world problems. The course starts by walking through different methods to prepare your data—be it a dataset with missing values or text columns that require the categories to be turned into indicator variables. After the data is ready, you'll learn different techniques aligned with different objectives—be it a dataset with known outcomes such as sales by state, or more complicated problems such as clustering similar customers. Finally, you'll learn how to polish your algorithm to ensure that it's both accurate and resilient to new datasets. You will learn to incorporate machine learning in your applications. Ranging from handwritten digit recognition to document classification, examples are solved step-by-step using scikit-learn and Python. By the end of this course you will have learned how to build applications that learn from experience, by applying the main concepts and techniques of machine learning.