Taylor Smith

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

For many Americans, debt has become overwhelming--and in some cases unmanageable. This book is a primer to understanding how debt works, why we borrow and what to do when debt gets beyond our reach. Debt has become woven into the fabric of most Americans' lives. We take out mortgages to buy our houses, borrow to pay for college, use it to own or lease our cars, and put purchases big and small on credit cards. From learning the ins and outs of the most common kinds of debt to understanding how interest rates and credit scores work, this book will give readers the information they need to maintain a healthy relationship with debt. And if debt gets out of control, this book offers concrete steps readers can take to get--and stay--out of debt. Plus: Easy-to-use worksheets to more easily manage spending and eliminate debt with money saving tips for everyone.

2020 notebook: 2020 notes journal, writing good vibes. Happy New year journal for a good beginning, listing noting. 6x9 po 100 pages

A collection of poems written from the soul. Heartbreak, loneliness, pain fear and loss. Emotions that we are can't show others. Come join me in the dark and together we can sit.

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 scratch, developing a deep understanding along the wayExplore 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) because it allows you to train your system to make pricing predictions, campaign adjustments, customer recommendations, and much more while the system self-adjusts and makes decisions on its own. As a result, it's 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 while facilitating a deep understanding along the way. You’ll embark on this journey with a quick overview and see how supervised machine learning differs from unsupervised learning. Next, we explore parametric models such as linear and logistic regression, non-parametric methods such as decision trees, and various clustering techniques to facilitate decision-making and predictions. As we proceed, 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 powerfully apply algorithms to new problems. What you will learnCrack how a machine learns a concept and generalize its understanding to 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 marketingExpand your expertise and use various 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 aspiring machine learning developers who want to get started with supervised learning. Intermediate knowledge of Python programming—and some fundamental knowledge of supervised learning—are expected.

Hipsters are a subculture of young people, typically in their teens, 20's and 30's that value independent thinking, counter-culture, an appreciation of art, a do-it-yourself lifestyle, and indie-rock, creativity, intelligence, and quirky style. The "hipster" culture extends from fashion choices to music to crafts and art. Hipster crafts embrace trends like ombre colors, fringe, feathers, geometric shapes and prints, mustaches, bacon, and quirky phrases like "Keep Calm and Craft On." Hipster crafts go back to the crafts of our grannies-crochet, sewing, painting, do-it-yourself- and make them fit the styles and designs of our generation. We called this ebook "Hipster Crafts" because we think it's a good way to describe our style of crafting. We're happy to roll with the term "hipster" because it means we embrace the fun and quirky trends that come and go. We like mustaches, geometric art, old comics, and geeky sci-fi vintage. Coming up with funny, counter-culture crafts and DIYs is what we enjoy doing, so the word hipster fits our art pretty well.