Deep Learning with TensorFlow. Explore neural networks and build intelligent systems with Python - Second Edition

Giancarlo Zaccone, Md. Rezaul Karim

Deep learning is a branch of machine learning algorithms based on learning multiple levels of abstraction. Neural networks, which are at the core of deep learning, are being used in predictive analytics, computer vision, natural language processing, time series forecasting, and to perform a myriad of other complex tasks.This book is conceived for developers, data analysts, machine learning practitioners and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries. Throughout the book, you’ll learn how to develop deep learning applications for machine learning systems using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Factorization Machines. Discover how to attain deep learning programming on GPU in a distributed way.You'll come away with an in-depth knowledge of machine learning techniques and the skills to apply them to real-world projects.

The Regularization Cookbook. Explore practical recipes to improve the functionality of your ML models

Vincent Vandenbussche, Akin Osman Kazakci

Regularization is an infallible way to produce accurate results with unseen data, however, applying regularization is challenging as it is available in multiple forms and applying the appropriate technique to every model is a must. The Regularization Cookbook provides you with the appropriate tools and methods to handle any case, with ready-to-use working codes as well as theoretical explanations. After an introduction to regularization and methods to diagnose when to use it, you’ll start implementing regularization techniques on linear models, such as linear and logistic regression, and tree-based models, such as random forest and gradient boosting. You’ll then be introduced to specific regularization methods based on data, high cardinality features, and imbalanced datasets. In the last five chapters, you’ll discover regularization for deep learning models. After reviewing general methods that apply to any type of neural network, you’ll dive into more NLP-specific methods for RNNs and transformers, as well as using BERT or GPT-3. By the end, you’ll explore regularization for computer vision, covering CNN specifics, along with the use of generative models such as stable diffusion and Dall-E.By the end of this book, you’ll be armed with different regularization techniques to apply to your ML and DL models.

Learning Continuous Integration with Jenkins. An end-to-end guide to creating operational, secure, resilient, and cost-effective CI/CD processes - Third Edition

Nikhil Pathania

This updated edition of Learning Continuous Integration with Jenkins is your one-stop guide to implementing CI/CD with Jenkins, addressing crucial technologies such as cloud computing, containerization, Infrastructure as Code, and GitOps. Tailored to both beginners and seasoned developers, the book provides a practical path to mastering a production-grade, secure, resilient, and cost-effective CI/CD setup.Starting with a detailed introduction to the fundamental principles of CI, this book systematically takes you through setting up a CI environment using Jenkins and other pivotal DevOps tools within the CI/CD ecosystem. You’ll learn to write pipeline code with AI assistance and craft your own CI pipeline. With the help of hands-on tutorials, you’ll gain a profound understanding of the CI process and Jenkins’ robust capabilities. Additionally, the book teaches you how to expand your CI pipeline with automated testing and deployment, setting the stage for continuous deployment. To help you through the complete software delivery process, this book also covers methods to ensure that your CI/CD setup is maintainable across teams, secure, and performs optimally.By the end of the book, you’ll have become an expert in implementing and optimizing CI/CD setups across diverse teams.

Microsoft SharePoint 2010 Business Application Blueprints. Master SharePoint application development by building exciting SharePoint business solutions with this book and

Mike Oryszak, Michael C Oryszak

SharePoint is an incredibly powerful platform that can support a wide variety of business scenarios. In many cases it needs to be configured or extended in order to deliver fully featured business solutions.While some books merely talk about the capabilities of SharePoint in general and leave you to figure out how they apply to your situation, this book takes a different approach. Each chapter provides easy-to-understand, step-by-step instructions along with screenshots to help build exciting SharePoint business solutions that extend the platform. By the end of this book the reader will be a SharePoint developer to be reckoned with.This book will dive into a diverse set of real-world scenarios to deliver sample business solutions that can serve as the foundation for your own solutions. This book draws from the author's extensive experience with SharePoint to leverage the platforms underlying services to provide solutions that can support Social Collaboration, Content and Document Management, as well as project collaboration. Each chapter represents a new business solution that builds on the overall platform to deliver more complex solutions and more advanced techniques. By the end of the book the reader will understand how to leverage the SharePoint platform to build their own business solutions.

A Blueprint for Production-Ready Web Applications. Leverage industry best practices to create complete web apps with Python, TypeScript, and AWS

Dr. Philip Jones

A Blueprint for Production-Ready Web Applications will help you expand upon your coding knowledge and teach you how to create a complete web application. Unlike other guides that focus solely on a singular technology or process, this book shows you how to combine different technologies and processes as needed to meet industry standards.You’ll begin by learning how to set up your development environment, and use Quart and React to create the backend and frontend, respectively. This book then helps you get to grips with managing and validating accounts, structuring relational tables, and creating forms to manage data. As you progress through the chapters, you’ll gain a comprehensive understanding of web application development by creating a to-do app, which can be used as a base for your future projects. Finally, you’ll find out how to deploy and monitor your application, along with discovering advanced concepts such as managing database migrations and adding multifactor authentication.By the end of this web development book, you’ll be able to apply the lessons and industry best practices that you’ve learned to both your personal and work projects, allowing you to further develop your coding portfolio.

GNS3 Network Simulation Guide. From installation through to creating large scale simulations, this is the complete guide to GNS3 that will give you the know-how needed for Cisco certification. For networking professionals, it's a career-advancing tutorial

GNS3, Christopher J Welsh (USD)

GNS3 is an open-source Graphical Network Simulator used extensively by networking professionals to simulate networking devices such as Cisco, Juniper and Vyatta routers, along with Virtual PCs.GNS3 Network Simulation Guide gives you all the information you need to run a successful GNS3 simulation. You will be guided through the tricky installation pre-requisites and procedures involved in developing a simulated topology suitable for studying for Cisco CCIE Certification. We will also cover many useful tips and tricks which will help you make the most of your GNS3 simulator application.You will begin the GNS3 journey by ensuring that you have prepared your computer with any additional files required by GNS3, to set up the simulated environment needed for a successful installation process.Once GNS3 is installed, you will get to work creating a successful simulation including routers, Ethernet switches, Virtual PCs, and capture packets using Wireshark. Next, you will learn how to master GNS3's Graphical User Interface as well as the Command Console. The inner workings of GNS3 are described in depth so that you gain an appreciation of how UDP tunnels are used to tie the many components of GNS3 together.By following the clear examples and exercises in this guide, you will learn everything that you need to know about how to use GNS3 to prepare for certifications, as well as simulate real-world network scenarios.

Learning JavaScript Data Structures and Algorithms. JavaScript Data Structures and algorithms can help you solve complex development problems – learn how by exploring a huge range of JavaScript data types

Loiane Groner

Principles of Data Science. A beginner's guide to essential math and coding skills for data fluency and machine learning - Third Edition

Sinan Ozdemir

Principles of Data Science bridges mathematics, programming, and business analysis, empowering you to confidently pose and address complex data questions and construct effective machine learning pipelines. This book will equip you with the tools to transform abstract concepts and raw statistics into actionable insights.Starting with cleaning and preparation, you’ll explore effective data mining strategies and techniques before moving on to building a holistic picture of how every piece of the data science puzzle fits together. Throughout the book, you’ll discover statistical models with which you can control and navigate even the densest or the sparsest of datasets and learn how to create powerful visualizations that communicate the stories hidden in your data.With a focus on application, this edition covers advanced transfer learning and pre-trained models for NLP and vision tasks. You’ll get to grips with advanced techniques for mitigating algorithmic bias in data as well as models and addressing model and data drift. Finally, you’ll explore medium-level data governance, including data provenance, privacy, and deletion request handling.By the end of this data science book, you'll have learned the fundamentals of computational mathematics and statistics, all while navigating the intricacies of modern ML and large pre-trained models like GPT and BERT.