Computer science Books

Book SynopsisBuild real-world time series forecasting systems which scale to millions of time series by applying modern machine learning and deep learning concepts Key Features Explore industry-tested machine learning techniques used to forecast millions of time series Get started with the revolutionary paradigm of global forecasting models Get to grips with new concepts by applying them to real-world datasets of energy forecasting Book DescriptionWe live in a serendipitous era where the explosion in the quantum of data collected and a renewed interest in data-driven techniques such as machine learning (ML), has changed the landscape of analytics, and with it, time series forecasting. This book, filled with industry-tested tips and tricks, takes you beyond commonly used classical statistical methods such as ARIMA and introduces to you the latest techniques from the world of ML. This is a comprehensive guide to analyzing, visualizing, and creating state-of-the-art forecasting systems, complete with common topics such as ML and deep learning (DL) as well as rarely touched-upon topics such as global forecasting models, cross-validation strategies, and forecast metrics. You’ll begin by exploring the basics of data handling, data visualization, and classical statistical methods before moving on to ML and DL models for time series forecasting. This book takes you on a hands-on journey in which you’ll develop state-of-the-art ML (linear regression to gradient-boosted trees) and DL (feed-forward neural networks, LSTMs, and transformers) models on a real-world dataset along with exploring practical topics such as interpretability. By the end of this book, you’ll be able to build world-class time series forecasting systems and tackle problems in the real world.What you will learn Find out how to manipulate and visualize time series data like a pro Set strong baselines with popular models such as ARIMA Discover how time series forecasting can be cast as regression Engineer features for machine learning models for forecasting Explore the exciting world of ensembling and stacking models Get to grips with the global forecasting paradigm Understand and apply state-of-the-art DL models such as N-BEATS and Autoformer Explore multi-step forecasting and cross-validation strategies Who this book is forThe book is for data scientists, data analysts, machine learning engineers, and Python developers who want to build industry-ready time series models. Since the book explains most concepts from the ground up, basic proficiency in Python is all you need. Prior understanding of machine learning or forecasting will help speed up your learning. For experienced machine learning and forecasting practitioners, this book has a lot to offer in terms of advanced techniques and traversing the latest research frontiers in time series forecasting.Table of ContentsTable of Contents Introducing Time Series Acquiring and Processing Time Series Data Analyzing and Visualizing Time Series Data Setting a Strong Baseline Forecast Time Series Forecasting as Regression Feature Engineering for Time Series Forecasting Target Transformations for Time Series Forecasting Forecasting Time Series with Machine Learning Models Ensembling and Stacking Global Forecasting Models Introduction to Deep Learning Building Blocks of Deep Learning for Time Series Common Modeling Patterns for Time Series Attention and Transformers for Time Series Strategies for Global Deep Learning Forecasting Models Specialized Deep Learning Architectures for Forecasting Multi-Step Forecasting Evaluating Forecasts – Forecast Metrics Evaluating Forecasts – Validation Strategies

Read more less

Book SynopsisDemystify causal inference and casual discovery by uncovering causal principles and merging them with powerful machine learning algorithms for observational and experimental data Purchase of the print or Kindle book includes a free PDF eBook Key Features Examine Pearlian causal concepts such as structural causal models, interventions, counterfactuals, and more Discover modern causal inference techniques for average and heterogenous treatment effect estimation Explore and leverage traditional and modern causal discovery methods Book DescriptionCausal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that elude a purely statistical mindset. Causal Inference and Discovery in Python helps you unlock the potential of causality. You’ll start with basic motivations behind causal thinking and a comprehensive introduction to Pearlian causal concepts, such as structural causal models, interventions, counterfactuals, and more. Each concept is accompanied by a theoretical explanation and a set of practical exercises with Python code. Next, you’ll dive into the world of causal effect estimation, consistently progressing towards modern machine learning methods. Step-by-step, you’ll discover Python causal ecosystem and harness the power of cutting-edge algorithms. You’ll further explore the mechanics of how “causes leave traces” and compare the main families of causal discovery algorithms. The final chapter gives you a broad outlook into the future of causal AI where we examine challenges and opportunities and provide you with a comprehensive list of resources to learn more.What you will learn Master the fundamental concepts of causal inference Decipher the mysteries of structural causal models Unleash the power of the 4-step causal inference process in Python Explore advanced uplift modeling techniques Unlock the secrets of modern causal discovery using Python Use causal inference for social impact and community benefit Who this book is forThis book is for machine learning engineers, data scientists, and machine learning researchers looking to extend their data science toolkit and explore causal machine learning. It will also help developers familiar with causality who have worked in another technology and want to switch to Python, and data scientists with a history of working with traditional causality who want to learn causal machine learning. It’s also a must-read for tech-savvy entrepreneurs looking to build a competitive edge for their products and go beyond the limitations of traditional machine learning.Table of ContentsTable of Contents Causality – Hey, We Have Machine Learning, So Why Even Bother? Judea Pearl and the Ladder of Causation Regression, Observations, and Interventions Graphical Models Forks, Chains, and Immoralities Nodes, Edges, and Statistical (In)dependence The Four-Step Process of Causal Inference Causal Models – Assumptions and Challenges Causal Inference and Machine Learning – from Matching to Meta-Learners Causal Inference and Machine Learning – Advanced Estimators, Experiments, Evaluations, and More Causal Inference and Machine Learning – Deep Learning, NLP, and Beyond Can I Have a Causal Graph, Please? Causal Discovery and Machine Learning – from Assumptions to Applications Causal Discovery and Machine Learning – Advanced Deep Learning and Beyond Epilogue

Read more less

Book SynopsisUnlock the true potential of software testing to achieve seamless software performance with this comprehensive guide Key Features Gain a solid understanding of software testing and master its multifaceted strategies Empower yourself to effectively overcome software testing challenges Develop actionable real-world testing skills for succeeding in any role Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionSoftware Testing Strategies covers a wide range of topics in the field of software testing, providing practical insights and strategies for professionals at every level. With equal emphasis on theoretical knowledge and practical application, this book is a valuable resource for programmers, testers, and anyone involved in software development. The first part delves into the fundamentals of software testing, teaching you about test design, tooling, and automation. The chapters help you get to grips with specialized testing areas, including security, internationalization, accessibility, and performance. The second part focuses on the integration of testing into the broader software delivery process, exploring different delivery models and puzzle pieces contributing to effective testing. You’ll discover how to craft your own test strategies and learn about lean approaches to software testing for optimizing processes. The final part goes beyond technicalities, addressing the broader context of testing. The chapters cover case studies, experience reports, and testing responsibilities, and discuss the philosophy and ethics of software testing. By the end of this book, you’ll be equipped to elevate your testing game and ensure software quality, and have an indispensable guide to the ever-evolving landscape of software quality assurance.What you will learn Explore accessibility, functional testing, performance testing, and more as an integral part of testing Find out how to implement a wide range of testing approaches Develop the skills needed to create effective testing strategies tailored to your project's needs Discover how to prioritize and execute the most impactful test ideas Gain insight into when and how to apply different testing elements Defend your chosen testing strategy with a comprehensive understanding of its components Who this book is forThis book is for a broad spectrum of professionals engaged in software development, including programmers, testers, and DevOps specialists. Tailored to those who aspire to elevate their testing practices beyond the basics, the book caters to anyone seeking practical insights and strategies to master the nuanced interplay between human intuition and automation. Whether you are a seasoned developer, meticulous tester, or DevOps professional, this comprehensive guide offers a transformative roadmap to become an adept strategist in the dynamic realm of software quality assurance.Table of ContentsTable of Contents Testing and Designing Tests Fundamental Issues in Tooling and Automation Programmer-Facing Testing Customer-Facing Tests Specialized Testing Testing Related Skills Test Data Management Delivery Models and Testing The Puzzle Pieces of Good Testing Putting Your Test Strategy Together Lean Software Testing Case Studies and Experience Reports Testing Activities or a Testing Role? Philosophy and Ethics in Software Testing Words and Language About Work Testing Strategy Applied

Read more less

Book SynopsisHarness the powerful Python programming language to navigate the realms of geographic information systems, remote sensing, topography, and more, while embracing a guiding framework for effective geospatial analysis Key Features Create GIS solutions using the new features introduced in Python 3.10 Explore a range of GIS tools and libraries, including PostGIS, QGIS, and PROJ Identify the tools and resources that best align with your specific needs Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionGeospatial analysis is used in almost every domain you can think of, including defense, farming, and even medicine. In this special 10th anniversary edition, you'll embark on an exhilarating geospatial analysis adventure using Python. This fourth edition starts with the fundamental concepts, enhancing your expertise in geospatial analysis processes with the help of illustrations, basic formulas, and pseudocode for real-world applications. As you progress, you’ll explore the vast and intricate geospatial technology ecosystem, featuring thousands of software libraries and packages, each offering unique capabilities and insights. This book also explores practical Python GIS geospatial applications, remote sensing data, elevation data, and the dynamic world of geospatial modeling. It emphasizes the predictive and decision-making potential of geospatial technology, allowing you to visualize complex natural world concepts, such as environmental conservation, urban planning, and disaster management to make informed choices. You’ll also learn how to leverage Python to process real-time data and create valuable information products. By the end of this book, you'll have acquired the knowledge and techniques needed to build a complete geospatial application that can generate a report and can be further customized for different purposes.What you will learn Automate geospatial analysis workflows using Python Understand the different formats in which geospatial data is available Unleash geospatial tech tools to create stunning visualizations Create thematic maps with Python tools such as PyShp, OGR, and the Python Imaging Library Build a geospatial Python toolbox for analysis and application development Unlock remote sensing secrets, detect changes, and process imagery Leverage ChatGPT for solving Python geospatial solutions Apply geospatial analysis to real-time data tracking and storm chasing Who this book is forThis book is for Python developers, researchers, or analysts who want to perform geospatial modeling and GIS analysis with Python. Basic knowledge of digital mapping and analysis using Python or other scripting languages will be helpful.Table of ContentsTable of Contents Learning about Geospatial Analysis with Python Learning about Geospatial Data The Geospatial Technology Landscape Geospatial Python Toolbox Python and Geospatial Algorithms Creating and Editing GIS Data Python and Remote Sensing Python and Elevation Data Advanced Geospatial Modeling Working with Real-Time Data Putting It All Together

Read more less

Book SynopsisA series of 51 papers forming the Proceedings of the 28th CAA Conference held at Ljubljana, Slovenia in 2000 focusing on computer applications and quantative methods in European and American archaeology.

Read more less

Book SynopsisThis excellent addition to the UTiCS series of undergraduate textbooks provides a detailed and up to date description of the main principles behind the design and implementation of modern programming languages.Rather than focusing on a specific language, the book identifies the most important principles shared by large classes of languages. To complete this general approach, detailed descriptions of the main programming paradigms, namely imperative, object-oriented, functional and logic are given, analysed in depth and compared. This provides the basis for a critical understanding of most of the programming languages.An historical viewpoint is also included, discussing the evolution of programming languages, and to provide a context for most of the constructs in use today. The book concludes with two chapters which introduce basic notions of syntax, semantics and computability, to provide a completely rounded picture of what constitutes a programming language.Trade ReviewFrom the reviews:“This undergraduate textbook on the principles of programming languages has many commendable aspects. It is grounded on sound principles of computing, with machines taking a central role. The authors use activation stacks and other machine-level abstractions to explain many complex ideas--such as scopes and evaluation mechanisms--in concrete terms. Furthermore, many aspects of C++, Java, and C# are covered and contrasted in substantial detail. … In short, what the text covers, it covers well … .” (Simon Thompson, ACM Computing Reviews, January, 2011)“This book provides a detailed description of the main principles behind the design and implementation of modern programming languages. … Primarily, the text is intended as a university textbook, but is also suitable for personal study of professionals who wish to deepen their knowledge of the mechanisms that lie behind the languages they use.” (Stefan Meyer, Zentralblatt MATH, Vol. 1204, 2011)Table of ContentsAbstract Machines.- How to Describe a Programming Language.- Foundations.- Names and The Environment.- Memory Management.- Control Structure.- Control Abstraction.- Structuring Data.- Data Abstraction.- The Object-Oriented Paradigm.- The Functional Paradigm.- The Logic Programming Paradigm.- A Short Historical Perspective.

Read more less

Book SynopsisThis pocket guide is designed to provide the reader with a basic understanding of how an organization's Information Technology supports and enables the achievement of its strategies and objectives. IT Governance recognizes that Information and Information Technology is at the heart of the modern economy - and at the heart of the modern business. It is a critical component of corporate governance and this pocket guide provides an introduction on how to approach this complex subject. This pocket guide describes the drivers for IT governance; why it matters; the relationship between IT governance, risk management, information risk, project governance and compliance risk; lists the symptoms of inadequate IT governance and the benefits that can be won by implementing an IT governance framework, and describes - in principle - how to go about doing this.Table of ContentsCONTENTS CHAPTER 1: Why IT Governance Matters 1 Governance background 1 IT governance defined 3 CHAPTER 2: Drivers for IT Governance 5 The information economy and intellectual capital 5 Competitiveness 7 Governance convergence 9 CHAPTER 3: Strategic and Operational Risk Management 13 Compliance risk 15 Information risk 18 Project governance 19 CHAPTER 4: Symptoms of Inadequate IT Governance 23 CHAPTER 5: What is in an IT Governance Framework? 25 IT steering committee 27 Enterprise IT architecture committee 30 IT audit 32 Third-party standards 33 CHAPTER 6: Benefits of an IT Governance Framework 35 CHAPTER 7: The Calder-Moir IT Governance Framework 37 Navigating the framework 39 Evaluate, direct, monitor 42 APPENDIX : IT Governance Resources 43

Read more less