Microsoft Defender for Identity in Depth. An exhaustive guide to ITDR, breach prevention, and cyberattack response

Pierre Thoor, Matthew Zorich

Written by a recognized cybersecurity expert, Microsoft Defender for Identity in Depth not only lays the groundwork for deploying and managing MDI, but also takes your knowledge to expert levels, enabling you to strengthen your organization against the most advanced cyber threats.You’ll familiarize yourself with the essentials of MDI, from seamless setup to leveraging PowerShell for automation, setting the stage for exploring advanced integrations and capabilities. Through practical, real-world examples, you’ll learn how to extend MDI’s reach by using APIs and conducting proactive threat hunting with KQL to turn insights into actions.The book gradually shifts focus to operational excellence, helping you develop expertise in investigating alerts, optimizing action accounts, and troubleshooting, which will empower you to master the building and maintenance of a robust ITDR framework and strengthen your security posture.By the end of this book, you’ll be able to harness the full potential of MDI’s functionalities, positioning you as a key player in your organization’s cybersecurity defenses.

The Artificial Intelligence Infrastructure Workshop. Build your own highly scalable and robust data storage systems that can support a variety of cutting-edge AI applications

Chinmay Arankalle, Gareth Dwyer, Bas Geerdink, Kunal Gera, ...

Social networking sites see an average of 350 million uploads daily - a quantity impossible for humans to scan and analyze. Only AI can do this job at the required speed, and to leverage an AI application at its full potential, you need an efficient and scalable data storage pipeline. The Artificial Intelligence Infrastructure Workshop will teach you how to build and manage one.The Artificial Intelligence Infrastructure Workshop begins taking you through some real-world applications of AI. You’ll explore the layers of a data lake and get to grips with security, scalability, and maintainability. With the help of hands-on exercises, you’ll learn how to define the requirements for AI applications in your organization. This AI book will show you how to select a database for your system and run common queries on databases such as MySQL, MongoDB, and Cassandra. You’ll also design your own AI trading system to get a feel of the pipeline-based architecture. As you learn to implement a deep Q-learning algorithm to play the CartPole game, you’ll gain hands-on experience with PyTorch. Finally, you’ll explore ways to run machine learning models in production as part of an AI application.By the end of the book, you’ll have learned how to build and deploy your own AI software at scale, using various tools, API frameworks, and serialization methods.

Hands-On Machine Learning with C++. Build, train, and deploy end-to-end machine learning and deep learning pipelines - Second Edition

Kirill Kolodiazhnyi

Written by a seasoned software engineer with several years of industry experience, this book will teach you the basics of machine learning (ML) and show you how to use C++ libraries, along with helping you create supervised and unsupervised ML models.You’ll gain hands-on experience in tuning and optimizing a model for various use cases, enabling you to efficiently select models and measure performance. The chapters cover techniques such as product recommendations, ensemble learning, anomaly detection, sentiment analysis, and object recognition using modern C++ libraries. You’ll also learn how to overcome production and deployment challenges on mobile platforms, and see how the ONNX model format can help you accomplish these tasks.This new edition has been updated with key topics such as sentiment analysis implementation using transfer learning and transformer-based models, as well as tracking and visualizing ML experiments with MLflow. An additional section shows you how to use Optuna for hyperparameter selection. The section on model deployment into mobile platform now includes a detailed explanation of real-time object detection for Android with C++.By the end of this C++ book, you’ll have real-world machine learning and C++ knowledge, as well as the skills to use C++ to build powerful ML systems.

The Complete Coding Interview Guide in Java. An effective guide for aspiring Java developers to ace their programming interviews

Anghel Leonard

Java is one of the most sought-after programming languages in the job market, but cracking the coding interview in this challenging economy might not be easy. This comprehensive guide will help you to tackle various challenges faced in a coding job interview and avoid common interview mistakes, and will ultimately guide you toward landing your job as a Java developer.This book contains two crucial elements of coding interviews - a brief section that will take you through non-technical interview questions, while the more comprehensive part covers over 200 coding interview problems along with their hands-on solutions. This book will help you to develop skills in data structures and algorithms, which technical interviewers look for in a candidate, by solving various problems based on these topics covering a wide range of concepts such as arrays, strings, maps, linked lists, sorting, and searching. You'll find out how to approach a coding interview problem in a structured way that produces faster results. Toward the final chapters, you'll learn to solve tricky questions about concurrency, functional programming, and system scalability.By the end of this book, you'll have learned how to solve Java coding problems commonly used in interviews, and will have developed the confidence to secure your Java-centric dream job.

Learning Dart. Dart is the programming language developed by Google that offers a new level of simple versatility. Learn all the essentials of Dart web development in this brilliant tutorial that takes you from beginner to pro

Ivo Balbaert, Dzenan Ridjanovic

DevOps Culture and Practice with OpenShift. Deliver continuous business value through people, processes, and technology

Tim Beattie, Mike Hepburn, Noel O'Connor, Donal Spring, ...

DevOps Culture and Practice with OpenShift features many different real-world practices - some people-related, some process-related, some technology-related - to facilitate successful DevOps, and in turn OpenShift, adoption within your organization. It introduces many DevOps concepts and tools to connect culture and practice through a continuous loop of discovery, pivots, and delivery underpinned by a foundation of collaboration and software engineering.Containers and container-centric application lifecycle management are now an industry standard, and OpenShift has a leading position in a flourishing market of enterprise Kubernetes-based product offerings. DevOps Culture and Practice with OpenShift provides a roadmap for building empowered product teams within your organization.This guide brings together lean, agile, design thinking, DevOps, culture, facilitation, and hands-on technical enablement all in one book. Through a combination of real-world stories, a practical case study, facilitation guides, and technical implementation details, DevOps Culture and Practice with OpenShift provides tools and techniques to build a DevOps culture within your organization on Red Hat's OpenShift Container Platform.

Mastering Adobe Captivate 8. Create responsive demonstrations, simulations, and quizzes for multiscreen delivery with Adobe Captivate

Damien Bruyndonckx

Machine Learning with Python. Unlocking AI Potential with Python and Machine Learning

Oliver Theobald

The course starts by setting the foundation with an introduction to machine learning, Python, and essential libraries, ensuring you grasp the basics before diving deeper. It then progresses through exploratory data analysis, data scrubbing, and pre-model algorithms, equipping you with the skills to understand and prepare your data for modeling.The journey continues with detailed walkthroughs on creating, evaluating, and optimizing machine learning models, covering key algorithms such as linear and logistic regression, support vector machines, k-nearest neighbors, and tree-based methods. Each section is designed to build upon the previous, reinforcing learning and application of concepts.Wrapping up, the course introduces the next steps, including an introduction to Python for newcomers, ensuring a comprehensive understanding of machine learning applications.