Taylor Smith – författare

Cloud native security isn't a game for individual players. It requires team collaboration with a platform that can help cloud security engineers, developers, and operations people do their best work. That's what the cloud native application protection platform (CNAPP) delivers. With this practical guide, you'll learn how CNAPPs can help you consolidate security through DevSecOps across cloud native technologies, practices, and application lifecycles.Through real-life attack scenarios, authors Russ Miles, Steve Giguere, and Taylor Smith help you explore how CNAPP not only mitigates multidimensional threats, but also reduces complexity and helps your team stay one step ahead of attackers. CNAPP provides a holistic approach to your cloud native development across identities, workloads, networks, and infrastructure.With this book, you will:Examine threats to different parts of the cloud native stack, including pipelines, supply chains, infrastructure, workloads, and applicationsLearn what CNAPP is and how it enables the context-sharing and collaboration necessary to secure your applications from development to runtimeAssess your own attack surface from a code and runtime standpointIdentify blind spots in your existing cloud native security coverageLeverage CNAPP to achieve a holistic, collaborative security environment

Teach your machine to think for itself!Key FeaturesDelve into supervised learning and grasp how a machine learns from dataImplement popular machine learning algorithms from scratchExplore some of the most popular scientific and mathematical libraries in the Python languageBook DescriptionSupervised machine learning is used in a wide range of sectors, such as finance, online advertising, and analytics, to train systems to make pricing predictions, campaign adjustments, customer recommendations, and much more by learning from the data that is used to train it and making decisions on its own. This makes it crucial to know how a machine 'learns' under the hood.This book will guide you through the implementation and nuances of many popular supervised machine learning algorithms, and help you understand how they work. You’ll embark on this journey with a quick overview of supervised learning and see how it differs from unsupervised learning. You’ll then explore parametric models, such as linear and logistic regression, non-parametric methods, such as decision trees, and a variety of clustering techniques that facilitate decision-making and predictions. As you advance, you'll work hands-on with recommender systems, which are widely used by online companies to increase user interaction and enrich shopping potential. Finally, you’ll wrap up with a brief foray into neural networks and transfer learning.By the end of this book, you’ll be equipped with hands-on techniques and will have gained the practical know-how you need to quickly and effectively apply algorithms to solve new problems.What you will learnCrack how a machine learns a concept and generalizes its understanding of new dataUncover the fundamental differences between parametric and non-parametric modelsImplement and grok several well-known supervised learning algorithms from scratchWork with models in domains such as ecommerce and marketingGet to grips with algorithms such as regression, decision trees, and clusteringBuild your own models capable of making predictionsDelve into the most popular approaches in deep learning such as transfer learning and neural networksWho this book is forThis book is for anyone who wants to get started with supervised learning. Intermediate knowledge of Python programming along with fundamental knowledge of supervised learning is expected.