Applying Math with Python. Over 70 practical recipes for solving real-world computational math problems - Second Edition

Sam Morley

The updated edition of Applying Math with Python will help you solve complex problems in a wide variety of mathematical fields in simple and efficient ways. Old recipes have been revised for new libraries and several recipes have been added to demonstrate new tools such as JAX.You'll start by refreshing your knowledge of several core mathematical fields and learn about packages covered in Python's scientific stack, including NumPy, SciPy, and Matplotlib. As you progress, you'll gradually get to grips with more advanced topics of calculus, probability, and networks (graph theory). Once you’ve developed a solid base in these topics, you’ll have the confidence to set out on math adventures with Python as you explore Python's applications in data science and statistics, forecasting, geometry, and optimization. The final chapters will take you through a collection of miscellaneous problems, including working with specific data formats and accelerating code.By the end of this book, you'll have an arsenal of practical coding solutions that can be used and modified to solve a wide range of practical problems in computational mathematics and data science.

Full-Stack Flask and React. Learn, code, and deploy powerful web applications with Flask 2 and React 18

Olatunde Adedeji

Developing an interactive, efficient, and fast enterprise web application requires both the right approach and tooling. If you are a web developer looking for a way to tap the power of React’s reusable UI components and the simplicity of Flask for backend development to develop production-ready, scalable web apps in Python, then this book is for you.Starting with an introduction to React, a JavaScript library for building highly interactive and reusable user interfaces, you’ll progress to data modeling for the web using SQLAlchemy and PostgreSQL, and then get to grips with Restful API development. This book will aid you in identifying your app users and managing access to your web application. You’ll also explore modular architectural design for Flask-based web applications and master error-handling techniques. Before you deploy your web app on AWS, this book will show you how to integrate unit testing best practices to ensure code reliability and functionality, making your apps not only efficient and fast but also robust and dependable.By the end of this book, you’ll have acquired deep knowledge of the Flask and React technology stacks, which will help you undertake web application development with confidence.

CMS Made Simple 1.6: Beginner's Guide. Create a fully functional and professional website using CMS Made Simple

Sofia Hauschildt, Ted Kulp (USD)

CMS Made Simple is a an open source content management system that allows rapid website development in a fraction of the normal time, avoiding hours of coding by providing modules and 3rd Party add-ons. With this book in hand you will be able to harness the power of this modular and extendable content management system at your fingertips.This guide for CMS Made Simple is based on practical and working solutions allowing you to understand how this powerful and simple application can support you in your daily work. The workshop helps you create engaging, effective, and easy-to-use CMS websites for businesses, clubs, or organizations.This is a step- by-step case study, aimed at helping you build a complete professional website with CMS Made Simple. You can take a ready-to-use template or implement your own custom design, enrich the website with features like a photo gallery, an e-commerce solution with PayPal checkout, and forms of any complexity or popular JQuery effects and finish it off by optimizing it for search engines. The useful HTML and CSS code snippets are optimized and can be easily adapted for your own projects. Chapter by chapter you will put yourself in the role of web designer, developer, administrator, and business manager, thus learning every aspect needed for building rich websites that are very simple to manage.

Data Labeling in Machine Learning with Python. Explore modern ways to prepare labeled data for training and fine-tuning ML and generative AI models

Vijaya Kumar Suda

Data labeling is the invisible hand that guides the power of artificial intelligence and machine learning. In today’s data-driven world, mastering data labeling is not just an advantage, it’s a necessity. Data Labeling in Machine Learning with Python empowers you to unearth value from raw data, create intelligent systems, and influence the course of technological evolution.With this book, you'll discover the art of employing summary statistics, weak supervision, programmatic rules, and heuristics to assign labels to unlabeled training data programmatically. As you progress, you'll be able to enhance your datasets by mastering the intricacies of semi-supervised learning and data augmentation. Venturing further into the data landscape, you'll immerse yourself in the annotation of image, video, and audio data, harnessing the power of Python libraries such as seaborn, matplotlib, cv2, librosa, openai, and langchain. With hands-on guidance and practical examples, you'll gain proficiency in annotating diverse data types effectively.By the end of this book, you’ll have the practical expertise to programmatically label diverse data types and enhance datasets, unlocking the full potential of your data.

Managing IaaS and DBaaS Clouds with Oracle Enterprise Manager Cloud Control 12c. Setting up a cloud environment is rarely smooth sailing but with this guide to Oracle Enterprise Manager Cloud Control, it just got a lot more manageable. Practical advice and lots of examples make it the ideal assistant

Ved Antani

Cloud computing has transformed the way that we write and deploy enterprise software. Oracle Enterprise Manager Cloud Control 12c has been designed to work with the cloud platform and reduce downtime, while improving performance and productivity. You can quickly set up, manage, and support enterprise clouds.This practical, example-oriented guide untangles many of the complexities involved in setting up a complete cloud computing platform. This book explores several methods of setting up IaaS and DBaaS using Oracle's Enterprise Manager. Step-by-step, this guide will quickly familiarize you with the most important aspects of setting up a cloud platform.This book delves deep into the complexities surrounding cloud computing and comprehensively explores the approach that you need to take to build an effective infrastructure. You will start with a step-by-step approach to building an Infrastructure as a Service (IaaS), and take an in-depth view of building a Database as a Service (DBaaS) model of cloud computing. Following on from this, you will learn how the chargeback mechanism works and how it can be configured for your needs. Next, you will also learn how to use a programmable interface to manage your cloud via APIs and web services.This guide will walk you through the various components of Oracle Enterprise Manager and will teach you how to use them efficiently. This book will also explain how you can use cloud APIs to program your cloud.

OpenJDK Cookbook. Over 80 recipes to build and extend your very own version of Java platform using OpenJDK project

Alex Kasko, Alexey Kashchenko, Alexey Mironchenko, Stanislav Kobylyanskiy

Responsible AI in the Enterprise. Practical AI risk management for explainable, auditable, and safe models with hyperscalers and Azure OpenAI

Adnan Masood, Heather Dawe, Ed Price, Dr. Ehsan Adeli

Responsible AI in the Enterprise is a comprehensive guide to implementing ethical, transparent, and compliant AI systems in an organization. With a focus on understanding key concepts of machine learning models, this book equips you with techniques and algorithms to tackle complex issues such as bias, fairness, and model governance. Throughout the book, you’ll gain an understanding of FairLearn and InterpretML, along with Google What-If Tool, ML Fairness Gym, IBM AI 360 Fairness tool, and Aequitas. You’ll uncover various aspects of responsible AI, including model interpretability, monitoring and management of model drift, and compliance recommendations. You’ll gain practical insights into using AI governance tools to ensure fairness, bias mitigation, explainability, privacy compliance, and privacy in an enterprise setting. Additionally, you’ll explore interpretability toolkits and fairness measures offered by major cloud AI providers like IBM, Amazon, Google, and Microsoft, while discovering how to use FairLearn for fairness assessment and bias mitigation. You’ll also learn to build explainable models using global and local feature summary, local surrogate model, Shapley values, anchors, and counterfactual explanations.By the end of this book, you’ll be well-equipped with tools and techniques to create transparent and accountable machine learning models.

JIRA Essentials. Use the features of JIRA to manage projects and effectively handle bugs and software issues

Patrick Li