Search results for "survival analysis with python"
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R 2,305.77
R 824.00
Hands-On GPU Computing with Python
Explore GPU-enabled programmable environment for machine learning, scientific applications, and gaming using PuCUDA, PyOpenGL, and Anaconda AccelerateKey FeaturesUnderstand effective synchronization strategies for faster processing using GPUsWrite parallel processing scripts with PyCuda and PyOpenCLLearn to use the CUDA libraries like CuDNN for deep learning on GPUsBook DescriptionGPUs are proving to be excellent general purpose-parallel computing solutions for high performance tasks such as deep learning and scientific computing.This book will be your guide to getting started with GPU computing. It will start with introducing GPU computing and explain the architecture and programming models for GPUs. You will learn, by example, how to perform GPU programming with Python, and you’ll look at using integrations such as PyCUDA, PyOpenCL, CuPy and Numba with Anaconda for various tasks such as machine learning and data mining. Going further, you will get to grips with GPU work flows, management, and deployment using modern containerization solutions. Toward the end of the book, you will get familiar with the principles of distributed computing for training machine learning models and enhancing efficiency and performance.By the end of this book, you will be able to set up a GPU ecosystem for running complex applications and data models that demand great processing capabilities, and be able to efficiently manage memory to compute your application effectively and quickly.What you will learnUtilize Python libraries and frameworks for GPU accelerationSet up a GPU-enabled programmable machine learning environment on your system with AnacondaDeploy your machine learning system on cloud containers with illustrated examplesExplore PyCUDA and PyOpenCL and compare them with platforms such as CUDA, OpenCL and ROCm.Perform data mining tasks with machine learning models on GPUsExtend your knowledge of GPU computing in scientific applicationsWho this book is forData Scientist, Machine Learning enthusiasts and professionals who wants to get started with GPU computation and perform the complex tasks with low-latency. Intermediate knowledge of Python programming is assumed.
R 1,498.00
Government Survival in Parliamentary Democracies
This book describes the results of a quantitative investigation into one of the central questions of political science: what determines how long governments survive in parliamentary democracies? Government survival is important because it constitutes an essential component of the overall functioning of parliamentary democracies; it is also closely associated with the introduction to the discipline of event history analysis, a highly promising statistical methodology. The investigation utilizes this methodology on what is undoubtedly the most comprehensive data set yet assembled on governments, comprising hundreds of variables measured for governments in sixteen West European parliamentary democracies over the entire post-war period to 1989. The results fundamentally challenge the central thread of theorizing on government survival and point to an alternative conceptualization of the relationship among governments, parties and voters.
R 4,966.07
R 1,069.00
Extreme Survival
Witness True Resilience in These Incredible Survival Stories“Michael Tougias converts the wisdom of survivors into advice we can all use...” —Amanda Ripley, bestselling author of The Unthinkable: Who Survives When Disaster Strikes—and Why#1 New Release in Disaster ReliefExtreme Survival will have you on the edge-of-your-seat with truly amazing survival stories.New York Times bestselling author Michael Tougias has earned critical acclaim and literary awards for his many best-seller non-fiction narratives. Extreme Survival is the long-awaited follow-up to The Finest Hours, co-authored with Casey Sherman.Explore the stories and the causes of manmade disasters. To answer the question of why disasters happen and how some survive, Tougias interviewed over 100 people who survived against all odds, first chronicling their harrowing survival stories, and then discussing in detail lessons learned. Extreme Survival delivers the entertainment and exceptional research Michael Tougias fans expect.Understand resilience through the mindset of survivors. Surviving disasters requires survival techniques to kick in at the right moment. Learn what a person is capable of when under extreme pressure and facing imminent disaster.Inside find:Captivating and narrative survival stories told in true Tougias trademark styleAnalysis of major man-made disasters and the faulty decisions that led to themFirst-person accounts and detailed survival tactics that can apply to your every-day lifeIf you like true survival books like The Greatest Survival Stories of All Time, Into Thin Air, The Gift of Fear, Into the Abyss, Deep Survival, or If I Live Until Morning, you’ll love Extreme Survival. Also don’t miss reading other Michael Tougais survival books, including Fatal Forecast, Ten Hours Until Dawn, A Storm Too Soon, and Overboard!
R 912.00
R 457.00
Numerical Methods in Physics with Python
Bringing together idiomatic Python programming, foundational numerical methods, and physics applications, this is an ideal standalone textbook for courses on computational physics. All the frequently used numerical methods in physics are explained, including foundational techniques and hidden gems on topics such as linear algebra, differential equations, root-finding, interpolation, and integration. The second edition of this introductory book features several new codes and 140 new problems (many on physics applications), as well as new sections on the singular-value decomposition, derivative-free optimization, Bayesian linear regression, neural networks, and partial differential equations. The last section in each chapter is an in-depth project, tackling physics problems that cannot be solved without the use of a computer. Written primarily for students studying computational physics, this textbook brings the non-specialist quickly up to speed with Python before looking in detail at the numerical methods often used in the subject.
R 2,353.00
Extreme Survival
Witness True Resilience in These Incredible Survival Stories“Michael Tougias converts the wisdom of survivors into advice we can all use...” —Amanda Ripley, bestselling author of The Unthinkable: Who Survives When Disaster Strikes—and Why#1 New Release in Disaster ReliefExtreme Survival will have you on the edge-of-your-seat with truly amazing survival stories.New York Times bestselling author Michael Tougias has earned critical acclaim and literary awards for his many best-seller non-fiction narratives. Extreme Survival is the long-awaited follow-up to The Finest Hours, co-authored with Casey Sherman.Explore the stories and the causes of manmade disasters. To answer the question of why disasters happen and how some survive, Tougias interviewed over 100 people who survived against all odds, first chronicling their harrowing survival stories, and then discussing in detail lessons learned. Extreme Survival delivers the entertainment and exceptional research Michael Tougias fans expect.Understand resilience through the mindset of survivors. Surviving disasters requires survival techniques to kick in at the right moment. Learn what a person is capable of when under extreme pressure and facing imminent disaster.Inside find:Captivating and narrative survival stories told in true Tougias trademark styleAnalysis of major man-made disasters and the faulty decisions that led to themFirst-person accounts and detailed survival tactics that can apply to your every-day lifeIf you like true survival books like The Greatest Survival Stories of All Time, Into Thin Air, The Gift of Fear, Into the Abyss, Deep Survival, or If I Live Until Morning, you’ll love Extreme Survival. Also don’t miss reading other Michael Tougais survival books, including Fatal Forecast, Ten Hours Until Dawn, A Storm Too Soon, and Overboard!
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Working with Survivor Siblings in Psychoanalysis
Working with Survivor Siblings in Psychoanalysis: Ability and Disability in Clinical Process explores a previously neglected area in the field of psychoanalysis, addressing undertheorized concepts on siblings, disabilities and psychic survivorship, and broadening our conceptualization of the enduring effects of lateral relations on human development. What happens to a person’s sense of self both personally and professionally when they grow up alongside a severely disabled sibling? Through a series of qualitative interviews held between the author and a sample of psychoanalysts, this book examines both the unconscious experience and the interpersonal field of survivor siblings. Through a trauma-informed contemporary psychoanalytic lens, Dobrich combines data analysis, theory-building, memoir, and clinical storytelling to explore and explicate the impact of lateral survivorship on the clinical moment, making room for a contemporary and nuanced appreciation of siblings in psychoanalysis.Working with Survivor Siblings in Psychoanalysis: Ability and Disability in Clinical Process, will be of immense interest and value to psychoanalysts and other mental health professionals, and for all therapists who work with and treat patients that are themselves survivor siblings. Uniquely integrating both academic and memoir writing, this book will also engage those building theory around the implications of the analyst’s subjectivity on clinical processes.
R 2,110.63
R 733.00
Machine Learning for Financial Risk Management with Python
Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, and risk analysts will explore Python-based machine learning and deep learning models for assessing financial risk. You'll learn how to compare results from ML models with results obtained by traditional financial risk models.Author Abdullah Karasan helps you explore the theory behind financial risk assessment before diving into the differences between traditional and ML models.Review classical time series applications and compare them with deep learning modelsExplore volatility modeling to measure degrees of risk, using support vector regression, neural networks, and deep learningRevisit and improve market risk models (VaR and expected shortfall) using machine learning techniquesDevelop a credit risk based on a clustering technique for risk bucketing, then apply Bayesian estimation, Markov chain, and other ML modelsCapture different aspects of liquidity with a Gaussian mixture modelUse machine learning models for fraud detectionIdentify corporate risk using the stock price crash metricExplore a synthetic data generation process to employ in financial risk
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Machine Learning for Time-Series with Python
Get better insights from time-series data and become proficient in model performance analysisKey FeaturesExplore popular and modern machine learning methods including the latest online and deep learning algorithmsLearn to increase the accuracy of your predictions by matching the right model with the right problemMaster time series via real-world case studies on operations management, digital marketing, finance, and healthcareBook DescriptionThe Python time-series ecosystem is huge and often quite hard to get a good grasp on, especially for time-series since there are so many new libraries and new models. This book aims to deepen your understanding of time series by providing a comprehensive overview of popular Python time-series packages and help you build better predictive systems. Machine Learning for Time-Series with Python starts by re-introducing the basics of time series and then builds your understanding of traditional autoregressive models as well as modern non-parametric models. By observing practical examples and the theory behind them, you will become confident with loading time-series datasets from any source, deep learning models like recurrent neural networks and causal convolutional network models, and gradient boosting with feature engineering. This book will also guide you in matching the right model to the right problem by explaining the theory behind several useful models. You’ll also have a look at real-world case studies covering weather, traffic, biking, and stock market data. By the end of this book, you should feel at home with effectively analyzing and applying machine learning methods to time-series.What you will learnUnderstand the main classes of time series and learn how to detect outliers and patternsChoose the right method to solve time-series problemsCharacterize seasonal and correlation patterns through autocorrelation and statistical techniquesGet to grips with time-series data visualizationUnderstand classical time-series models like ARMA and ARIMAImplement deep learning models, like Gaussian processes, transformers, and state-of-the-art machine learning modelsBecome familiar with many libraries like Prophet, XGboost, and TensorFlowWho this book is forThis book is ideal for data analysts, data scientists, and Python developers who want instantly useful and practical recipes to implement today, and a comprehensive reference book for tomorrow. Basic knowledge of the Python Programming language is a must, while familiarity with statistics will help you get the most out of this book.
R 1,889.00
Build Production-Ready AI Agents with the Microsoft Stack
Build AI agents across Copilot Studio, Microsoft Foundry, and Agent Framework, from tenant setup and deployment to multimodal assessment, code-first workflows, evidence-based decisions, and production architecture choicesKey FeaturesBuild grounded, multimodal, and multi-agent solutions across three Microsoft agent tiersEvaluate, secure, publish, and monitor agents using production-focused workflowsImplement durable human-in-the-loop orchestration with checkpoints and auditable decisionsBook DescriptionMoving an AI agent from a working prototype to a production system requires more than a good prompt. You need the right architecture, reliable grounding, controlled tool use, evaluation, security, observability, and clear points for human intervention. Build Production-Ready AI Agents with the Microsoft Stack shows how these pieces fit together through through Claims 365, a fictional insurer's claims process.You will build a grounded business agent with Copilot Studio, secure and evaluate it, and publish it for real use. You will then create a multimodal assessment agent in Microsoft Foundry that processes images and documents and returns structured outputs. From there, you will run, test, and resume a complete code-first multi-agent workflow built with Microsoft Agent Framework, in Python or C#. It coordinates agents and tools to gather and challenge evidence, then pauses for durable human approval using checkpoints, shared state, Dataverse, and an auditable investigation trail.The book concludes by comparing the three Microsoft agent tiers (Copilot Studio, Microsoft Foundry, and Microsoft Agent Framework) and the trade-offs between flexibility, control, maintenance, and operational responsibility, helping you select the right architecture for different production scenarios.What you will learnChoose the right Microsoft agent tier for each workloadGround agent responses in Dataverse and business dataEvaluate grounded responses and identify failure casesSecure and publish agents with sign-in and entitlement checksBuild multimodal agents with structured JSON outputsDesign code-first multi-agent workflows with shared stateAdd durable human approval with checkpoints and audit trailsWho this book is forThis book is for solution architects, AI engineers, developers, technical consultants, and Microsoft platform professionals who need to take agentic solutions beyond prototypes. It is especially useful for readers working across Copilot Studio, Microsoft Foundry, and Microsoft Agent Framework who need to make architecture, security, evaluation, orchestration, and human-oversight decisions. Familiarity with Microsoft cloud services and application development will help readers get the most from the code-first sections.
R 1,140.00
R 1,130.00
Environmental Data Analysis
Environmental Data Analysis is an introductory statistics textbook for environmental science. It covers descriptive, inferential and predictive statistics, centred on the Generalized Linear Model. The key idea behind this book is to approach statistical analyses from the perspective of maximum likelihood, essentially treating most analyses as (multiple) regression problems. The reader will be introduced to statistical distributions early on, and will learn to deploy models suitable for the data at hand, which in environmental science are often not normally distributed. To make the initially steep learning curve more manageable, each statistical chapter is followed by a walk-through in a corresponding R-based how-to chapter, which reviews the theory and applies it to environmental data. In this way, a coherent and expandable foundation in parametric statistics is laid, which can be expanded in advanced courses.The content has been “field-tested” in several years of courses on statistics for Environmental Science, Geography and Forestry taught at the University of Freiburg.
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