Machine Learning Security with Azure. Best practices for assessing, securing, and monitoring Azure Machine Learning workloads

Georgia Kalyva, George Kavvalakis

With AI and machine learning (ML) models gaining popularity and integrating into more and more applications, it is more important than ever to ensure that models perform accurately and are not vulnerable to cyberattacks. However, attacks can target your data or environment as well. This book will help you identify security risks and apply the best practices to protect your assets on multiple levels, from data and models to applications and infrastructure.This book begins by introducing what some common ML attacks are, how to identify your risks, and the industry standards and responsible AI principles you need to follow to gain an understanding of what you need to protect. Next, you will learn about the best practices to secure your assets. Starting with data protection and governance and then moving on to protect your infrastructure, you will gain insights into managing and securing your Azure ML workspace. This book introduces DevOps practices to automate your tasks securely and explains how to recover from ML attacks. Finally, you will learn how to set a security benchmark for your scenario and best practices to maintain and monitor your security posture.By the end of this book, you’ll be able to implement best practices to assess and secure your ML assets throughout the Azure Machine Learning life cycle.

Leaflet.js Essentials. Create interactive, mobile-friendly mapping applications using the incredibly light yet powerful Leaflet.js platform

Paul Crickard III, Paul Crickard

50 Algorithms Every Programmer Should Know. Tackle computer science challenges with classic to modern algorithms in machine learning, software design, data systems, and cryptography - Second Edition

Imran Ahmad, Somaieh Nikpoor

The ability to use algorithms to solve real-world problems is a must-have skill for any developer or programmer. This book will help you not only to develop the skills to select and use an algorithm to tackle problems in the real world but also to understand how it works.You'll start with an introduction to algorithms and discover various algorithm design techniques, before exploring how to implement different types of algorithms, with the help of practical examples. As you advance, you'll learn about linear programming, page ranking, and graphs, and will then work with machine learning algorithms to understand the math and logic behind them.Case studies will show you how to apply these algorithms optimally before you focus on deep learning algorithms and learn about different types of deep learning models along with their practical use.You will also learn about modern sequential models and their variants, algorithms, methodologies, and architectures that are used to implement Large Language Models (LLMs) such as ChatGPT.Finally, you'll become well versed in techniques that enable parallel processing, giving you the ability to use these algorithms for compute-intensive tasks.By the end of this programming book, you'll have become adept at solving real-world computational problems by using a wide range of algorithms.

Machine Learning Automation with TPOT. Build, validate, and deploy fully automated machine learning models with Python

Dario Radečić

The automation of machine learning tasks allows developers more time to focus on the usability and reactivity of the software powered by machine learning models. TPOT is a Python automated machine learning tool used for optimizing machine learning pipelines using genetic programming. Automating machine learning with TPOT enables individuals and companies to develop production-ready machine learning models cheaper and faster than with traditional methods.With this practical guide to AutoML, developers working with Python on machine learning tasks will be able to put their knowledge to work and become productive quickly. You'll adopt a hands-on approach to learning the implementation of AutoML and associated methodologies. Complete with step-by-step explanations of essential concepts, practical examples, and self-assessment questions, this book will show you how to build automated classification and regression models and compare their performance to custom-built models. As you advance, you'll also develop state-of-the-art models using only a couple of lines of code and see how those models outperform all of your previous models on the same datasets.By the end of this book, you'll have gained the confidence to implement AutoML techniques in your organization on a production level.

WiX Cookbook. Over 60 hands-on recipes packed with tips and tricks to boost your Windows installations

Nicholas Matthew Ramirez, Nick Ramirez

Hands-On Generative Adversarial Networks with Keras. Your guide to implementing next-generation generative adversarial networks

Rafael Valle

Generative Adversarial Networks (GANs) have revolutionized the fields of machine learning and deep learning. This book will be your first step toward understanding GAN architectures and tackling the challenges involved in training them.This book opens with an introduction to deep learning and generative models and their applications in artificial intelligence (AI). You will then learn how to build, evaluate, and improve your first GAN with the help of easy-to-follow examples. The next few chapters will guide you through training a GAN model to produce and improve high-resolution images. You will also learn how to implement conditional GANs that enable you to control characteristics of GAN output. You will build on your knowledge further by exploring a new training methodology for progressive growing of GANs. Moving on, you'll gain insights into state-of-the-art models in image synthesis, speech enhancement, and natural language generation using GANs. In addition to this, you'll be able to identify GAN samples with TequilaGAN.By the end of this book, you will be well-versed with the latest advancements in the GAN framework using various examples and datasets, and you will have developed the skills you need to implement GAN architectures for several tasks and domains, including computer vision, natural language processing (NLP), and audio processing.Foreword by Ting-Chun Wang, Senior Research Scientist, NVIDIA

Django 1.0 Website Development. Build powerful web applications, quickly and cleanly, with the Django application framework

Ayman Hourieh, Jacob Kaplan-Moss, Ayman Hourieh (EUR)

Django is a high-level Python web framework that was developed by a fast-moving online-news operation to meet the stringent twin challenges of newsroom deadlines and the needs of web developers. It is designed to promote rapid development and clean, pragmatic design and lets you build high-performing, elegant web applications rapidly. Django focuses on automating as much as possible and adhering to the DRY (Don't Repeat Yourself) principle, making it easier to build high-performance web applications faster, with less code.This book will show you how to assemble Django's features and take advantage of its power to design, develop, and deploy a fully-featured web site. It will walk you through the creation of an example web application, with lots of code examples.Specially revised for version 1.0 of Django, the book starts by introducing the main design concepts in Django. Next, it leads you through the process of installing Django on your system. After that, you will start right away on building your social bookmarking application using Django. Various Django 1.0 components and sub-frameworks will be explained during this process, and you will learn about them by example.In each chapter, you will build one or more of the features that are essential in Web 2.0 applications, like user management, tags, and AJAX. You will also learn about good software development practices, such as keeping your application secure, and automating testing with unit tests. By the end of the book, you will have built a fully functional real-life Web 2.0 application, and learned how to deploy it to a production server.

OpenLayers 3.x Cookbook. This book will provide users with a variety of recipes that illustrate different features present in OpenLayers 3 - Second Edition

Peter J. Langley, Antonio Santiago Perez

OpenLayers 3 is one of the most important and complete open source JavaScript mapping libraries today. Throughout this book, you will go through recipes that expose various features of OpenLayers 3, allowing you to gain an insight into building complex GIS web applications.You will get to grips with the basics of creating a map with common functionality and quickly advance to more complicated solutions that address modern challenges. You will explore into maps, raster and vector layers, and styling in depth. This book also includes problem solving and how-to recipes for the most common and important tasks.