Informatyka
Sinchan Banerjee
Java architectural patterns and tools help architects to build reliable, scalable, and secure data engineering solutions that collect, manipulate, and publish data.This book will help you make the most of the architecting data solutions available with clear and actionable advice from an expert.You’ll start with an overview of data architecture, exploring responsibilities of a Java data architect, and learning about various data formats, data storage, databases, and data application platforms as well as how to choose them. Next, you’ll understand how to architect a batch and real-time data processing pipeline. You’ll also get to grips with the various Java data processing patterns, before progressing to data security and governance. The later chapters will show you how to publish Data as a Service and how you can architect it. Finally, you’ll focus on how to evaluate and recommend an architecture by developing performance benchmarks, estimations, and various decision metrics.By the end of this book, you’ll be able to successfully orchestrate data architecture solutions using Java and related technologies as well as to evaluate and present the most suitable solution to your clients.
Scales of Banach Spaces, Theory of Interpolation and their Applications
Łukasz Dawidowski
Celem niniejszej monografii jest omówienie teorii skal przestrzeni Banacha oraz teorii interpolacji wraz z podaniem przykładów ich zastosowań. W pierwszej kolejności opisano teoretyczne podstawy teorii interpolacji. Podano definicje oraz podstawowe twierdzenia dotyczące konstrukcji przestrzeni interpolacyjnych (interpolacja rzeczywista i zespolona). Druga, główna, część monografii przedstawia definicję potęg ułamkowych operatorów, w szczególności dodatnich operatorów sektorialnych. Zaprezentowano także ich zastosowanie do konstrukcji skal przestrzeni Banacha, które jako główny obiekt badań są przykładem przestrzeni interpolacyjnych. W pracy zamieszczono również charakteryzację skal przestrzeni Banacha, która służy jako podstawa teoretyczna do opisu zastosowań tej teorii. W trzeciej części pokazano wykorzystanie podanej wcześniej teorii do badania „zachowań” operatorów na różnych poziomach skali. Udowodniono twierdzenia dotyczące operatorów domkniętych oraz operatorów sektorialnych. Następnie opisano konkretne równania cząstkowe, w rozwiązywaniu których można zastosować wspomnianą teorię.
Scientific Computing with Python 3. Click here to enter text
Claus Führer, Jan Erik Solem, Olivier Verdier
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.
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
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.
Michael Badger, Lifelong Kindergarten Group
If you have the imaginative power to design complex multimedia projects but can't adapt to programming languages, then Scratch 1.4: Beginner's Guide is the book for you. Imagine how good you'll feel when you drag-and-drop your way to interactive games, stories, graphic artwork, computer animations, and much more using Scratch even if you have never programmed before.This book provides teachers, parents, and new programmers with a guided tour of Scratch's features by creating projects that can be shared, remixed, and improved upon in your own lesson plans. Soon you will be creating games, stories, and animations by snapping blocks of code together.When you program you solve problems. In order to solve problems, you think, take action, and reflect upon your efforts. Scratch teaches you to program using a fun, accessible environment that's as easy as dragging and dropping blocks from one part of the screen to another.In this book you will program games, stories, and animations using hands-on examples that get you thinking and tinkering. For each project, you start with a series of steps to build something. Then you pause to put our actions into context so that you can relate our code to the actions on Scratch's stage. Throughout each chapter, you'll encounter challenges that encourage you to experiment and learn.One of the things you're really going to love is that, as you begin working through the examples in the book, you won't be able to stop your imagination and the ideas will stream as fast as you can think of them. Write them down. You'll quickly realize there are a lot of young minds in your home, classroom, or community group that could benefit from Scratch's friendly face. Teach them, please.