Using Stable Diffusion with Python. Leverage Python to control and automate high-quality AI image generation using Stable Diffusion

Andrew Zhu (Shudong Zhu), Matthew Fisher

Stable Diffusion is a game-changing AI tool that enables you to create stunning images with code. The author, a seasoned Microsoft applied data scientist and contributor to the Hugging Face Diffusers library, leverages his 15+ years of experience to help you master Stable Diffusion by understanding the underlying concepts and techniques.You’ll be introduced to Stable Diffusion, grasp the theory behind diffusion models, set up your environment, and generate your first image using diffusers. You'll optimize performance, leverage custom models, and integrate community-shared resources like LoRAs, textual inversion, and ControlNet to enhance your creations. Covering techniques such as face restoration, image upscaling, and image restoration, you’ll focus on unlocking prompt limitations, scheduled prompt parsing, and weighted prompts to create a fully customized and industry-level Stable Diffusion app. This book also looks into real-world applications in medical imaging, remote sensing, and photo enhancement. Finally, you'll gain insights into extracting generation data, ensuring data persistence, and leveraging AI models like BLIP for image description extraction.By the end of this book, you'll be able to use Python to generate and edit images and leverage solutions to build Stable Diffusion apps for your business and users.

Python Data Analysis Cookbook. Clean, scrape, analyze, and visualize data with the power of Python!

Ivan Idris

Data analysis is a rapidly evolving field and Python is a multi-paradigm programming language suitable for object-oriented application development and functional design patterns. As Python offers a range of tools and libraries for all purposes, it has slowly evolved as the primary language for data science, including topics on: data analysis, visualization, and machine learning.Python Data Analysis Cookbook focuses on reproducibility and creating production-ready systems. You will start with recipes that set the foundation for data analysis with libraries such as matplotlib, NumPy, and pandas. You will learn to create visualizations by choosing color maps and palettes then dive into statistical data analysis using distribution algorithms and correlations. You’ll then help you find your way around different data and numerical problems, get to grips with Spark and HDFS, and then set up migration scripts for web mining.In this book, you will dive deeper into recipes on spectral analysis, smoothing, and bootstrapping methods. Moving on, you will learn to rank stocks and check market efficiency, then work with metrics and clusters. You will achieve parallelism to improve system performance by using multiple threads and speeding up your code.By the end of the book, you will be capable of handling various data analysis techniques in Python and devising solutions for problem scenarios.

Lo-Dash Essentials. Implement fast, lean, and readable code effectively with Lo-Dash

Adam Boduch

Automating DevOps with GitLab CI/CD Pipelines. Build efficient CI/CD pipelines to verify, secure, and deploy your code using real-life examples

Christopher Cowell, Nicholas Lotz, Chris Timberlake

Developers and release engineers understand the high stakes involved in building, packaging, and deploying code correctly. Ensuring that your code is functionally correct, fast, and secure is a time-consuming and complex task. Code implementation, development, and deployment can be conducted efficiently using GitLab CI/CD pipelines.Automating DevOps with GitLab CI/CD Pipelines begins with the basics of Git and GitLab, showing how to commit and review code. You’ll learn to set up GitLab Runners for executing and autoscaling CI/CD pipelines and creating and configuring pipelines for many software development lifecycle steps. You'll also discover where to find pipeline results in GitLab, and how to interpret those results. Through the course of the book, you’ll become well-equipped with deploying code to different environments, advancing CI/CD pipeline features such as connecting GitLab to a Kubernetes cluster and using GitLab with Terraform, triggering pipelines and improving pipeline performance and using best practices and troubleshooting tips for uncooperative pipelines. In-text examples, use cases, and self-assessments will reinforce the important CI/CD, GitLab, and Git concepts, and help you prepare for interviews and certification exams related to GitLab.By the end of this book, you'll be able to use GitLab to build CI/CD pipelines that automate all the DevOps steps needed to build and deploy high-quality, secure code.

Network Automation with Go. Learn how to automate network operations and build applications using the Go programming language

Nicolas Leiva, Michael Kashin

Go’s built-in first-class concurrency mechanisms make it an ideal choice for long-lived low-bandwidth I/O operations, which are typical requirements of network automation and network operations applications.This book provides a quick overview of Go and hands-on examples within it to help you become proficient with Go for network automation. It’s a practical guide that will teach you how to automate common network operations and build systems using Go.The first part takes you through a general overview, use cases, strengths, and inherent weaknesses of Go to prepare you for a deeper dive into network automation, which is heavily reliant on understanding this programming language. You’ll explore the common network automation areas and challenges, what language features you can use in each of those areas, and the common software tools and packages. To help deepen your understanding, you’ll also work through real-world network automation problems and apply hands-on solutions to them.By the end of this book, you’ll be well-versed with Go and have a solid grasp on network automation.

Learning NServiceBus Sagas. Discover how to design, build, and test sagas and messaging with NServiceBus

Richard L Helton

Interpretable Machine Learning with Python. Learn to build interpretable high-performance models with hands-on real-world examples

Serg Masís

Do you want to gain a deeper understanding of your models and better mitigate poor prediction risks associated with machine learning interpretation? If so, then Interpretable Machine Learning with Python deserves a place on your bookshelf.We’ll be starting off with the fundamentals of interpretability, its relevance in business, and exploring its key aspects and challenges. As you progress through the chapters, you'll then focus on how white-box models work, compare them to black-box and glass-box models, and examine their trade-off. You’ll also get you up to speed with a vast array of interpretation methods, also known as Explainable AI (XAI) methods, and how to apply them to different use cases, be it for classification or regression, for tabular, time-series, image or text. In addition to the step-by-step code, this book will also help you interpret model outcomes using examples. You’ll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods you’ll explore here range from state-of-the-art feature selection and dataset debiasing methods to monotonic constraints and adversarial retraining.By the end of this book, you'll be able to understand ML models better and enhance them through interpretability tuning.

Building Smart Home Automation Solutions with Home Assistant. Configure, integrate, and manage hardware and software systems to automate your home

Marco Carvalho

Picture a home where you can adjust the lighting based on the time of day or when movement is detected. In this same home, you can also detect when a door is unexpectedly opened or an alarm is triggered in response to any suspicious activity. Such automated devices form part of a smart home, and the exciting part is that this book teaches you how to create and manage these devices all by yourself.This book helps you create your own ecosystem to automate your home using Home Assistant software. You’ll begin by understanding the components of a home automation system and learn how to create, hack, and configure them to operate seamlessly. Then, you'll set up Home Assistant on a Raspberry Pi to work as a home automation server, build your own IoT sensors based on ESP32/ESP8266, and set up real-life automation use cases using hands-on examples and projects. The chapters will also guide you in using software tools such as Node-RED, InfluxDB, and Grafana to manage, present, and use data collected from your Home Automation devices. Finally, you’ll gain insights into new technologies and trends in the home automation space to help you continue with your learning journey.By the end of this book, you’ll be able to build your own creative, IoT-based home automation system using different hardware and software technologies.