Publisher: K-i-s-publishing
Joshua Alan Teter, Ben Tobin
The technical program manager (TPM) is a relatively new role born out of the need of the tech industry to have a specialized practitioner who speaks both tech and business and leverages this bilingual talent to get results that no one else can.This book dives into what makes a TPM tick. You’ll find out which project and program management skills will help you shine and how you can apply your technical skills for effective results. This book looks at the TPM role across the Big Five tech companies (Amazon, Google, Microsoft, Apple, and Meta) to help you discern the most effective skills to be successful no matter which company you work for.Are you already a well-performing TPM looking to see what’s next? This book identifies the career paths for a TPM at the Big Five to help you decide the next step for you.By the end of this book, you’ll have a clear understanding of how to be a TPM, along with a breakdown of the necessary technical and program management skills to develop a clear roadmap for your career.
Joshua Alan Teter
The role of the Technical Program Manager (TPM) is crucial in the tech industry, bridging the gap between technical expertise and business acumen. The second edition of the Technical Program Manager’s Handbook offers a comprehensive guide to excelling in this role. Building on the foundational concepts from the first edition, this updated version introduces new chapters that explore the integration of AI and Emotional Intelligence (EI) in program management. These crucial additions complement the broader focus of the book on bridging business and technology but don’t overshadow it. You'll learn how to apply AI-driven tools to enhance project planning, risk management, and stakeholder communication, as well as how to leverage EI to build strong team relationships and improve leadership effectiveness.This book provides practical tools, methodologies, and case studies, offering insights from industry leaders to help both aspiring and practicing TPMs apply advanced concepts and strategies effectively. It also explores the unique aspects of the TPM role in major tech companies, helping you to navigate and thrive in your career.By the end of this book, you'll have a clear understanding of the TPM role, along with a breakdown of the necessary technical and program management skills, to develop a clear roadmap for your career.*Email sign-up and proof of purchase required
Chris Chinchilla
Effective documentation is key to the success of products in remote software development teams, facilitating clear instructions that benefit the entire development team. Technical Writing for Software Developers lays a solid foundation of essential grammar, providing language tips and explaining how precise writing enhances documentation, and walks you through the fundamental types and styles of documentation.Starting with an exploration of the current state of the tech writing industry and its significance in both the software and hardware realms, you’ll master the building blocks of technical writing, exploring tooling choices and style guides, and create dynamic multimedia-laden documentation. This book equips you with valuable insights into the writing and feedback process to ensure continuous improvement. Additionally, you’ll take a peek at the emerging trends and technologies, including AI tools, shaping the future of technical writing.By the end of this technical writing book, you’ll have developed the expertise you need to tackle documentation requests effectively, armed with the knowledge of the best approach for documenting any topic, encompassing text, media elements, structure, and appropriate tools. The skills acquired will enable you to achieve seamless teamwork, enhanced project efficiency, and successful software development.
Ahilan Ponnusamy, Andreas Spanner, Mirco Hering, Vincent...
Cloud goals, such as faster time to market, lower total cost of ownership (TCO), capex reduction, self-service enablement, and complexity reduction are important, but organizations often struggle to achieve the desired outcomes. With edge computing gaining momentum across industries and making it possible to move workloads seamlessly between cloud and edge locations, organizations need working recipes to find ways of extracting the most value out of their cloud and edge estate.This book provides a practical way to build a strategy-aligned operating model while considering various related factors such as culture, leadership, team structures, metrics, intrinsic motivators, team incentives, tenant experience, platform engineering, operations, open source, and technology choices. Throughout the chapters, you’ll discover how single, hybrid, or multicloud architectures, security models, automation, application development, workload deployments, and application modernization can be reutilized for edge workloads to help you build a secure yet flexible technology operating model. The book also includes a case study which will walk you through the operating model build process in a step-by-step way.By the end of this book, you’ll be able to build your own fit-for-purpose distributed technology operating model for your organization in an open culture way.
Marius Bancila
Learn how the metaprogramming technique enables you to create data structures and functions that allow computation to happen at compile time. With this book, you'll realize how templates help you avoid writing duplicate code and are key to creating generic libraries, such as the standard library or Boost, that can be used in a multitude of programs.The introductory chapters of this book will give you insights into the fundamentals of templates and metaprogramming. You'll then move on to practice writing complex templates and exploring advanced concepts such as template recursion, template argument deduction, forwarding references, type traits, and conditional compilation. Along the way, you'll learn how to write variadic templates and how to provide requirements to the template arguments with C++20 constraints and concepts. Finally, you'll apply your knowledge of C++ metaprogramming templates to implement various metaprogramming patterns and techniques.By the end of this book, you'll have learned how to write effective templates and implement metaprogramming in your everyday programming journey.
Ten Steps to ITSM Success. A Practitioner's Guide to Enterprise IT Transformation
IT Governance Publishing, Angelo Esposito, Timothy Rogers
This book provides a strategic framework for ITSM implementation, focusing on structured planning, role definition, process standardization, and ongoing performance assessments. With each chapter focusing on a critical aspect of ITSM, readers will gain the tools and knowledge needed to optimize service management practices and achieve long-term success. By the end of the book, IT leaders will understand how to build a robust ITSM program that aligns with business goals and drives continuous improvement.The book begins by setting the stage for ITSM implementation, helping readers assess the current state of services and plan their next steps. As the journey progresses, the book delves deeper into organizational changes, process development, and defining roles, ensuring that all stakeholders are aligned with the ITSM strategy.In the final chapters, the book focuses on deployment, continual improvement, and the importance of using metrics such as the balanced scorecard to ensure the ITSM framework is sustainable and continuously evolving. Readers will also learn how to handle operational sustainment and improve service delivery over time.
Antonio Gulli, Amita Kapoor
Deep neural networks (DNNs) have achieved a lot of success in the field of computer vision, speech recognition, and natural language processing. This exciting recipe-based guide will take you from the realm of DNN theory to implementing them practically to solve real-life problems in the artificial intelligence domain.In this book, you will learn how to efficiently use TensorFlow, Google’s open source framework for deep learning. You will implement different deep learning networks, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Q-learning Networks (DQNs), and Generative Adversarial Networks (GANs), with easy-to-follow standalone recipes. You will learn how to use TensorFlow with Keras as the backend. You will learn how different DNNs perform onsome popularly used datasets, such as MNIST, CIFAR-10, and Youtube8m. You will not only learn about the different mobile and embedded platforms supported by TensorFlow, but also how to set up cloud platforms for deep learning applications. You will also get a sneak peek at TPU architecture and how it will affect the future of DNNs.By using crisp, no-nonsense recipes, you will become an expert in implementing deep learning techniques in growing real-world applications and research areas such as reinforcement learning,GANs, and autoencoders.
Palanisamy P
With deep reinforcement learning, you can build intelligent agents, products, and services that can go beyond computer vision or perception to perform actions. TensorFlow 2.x is the latest major release of the most popular deep learning framework used to develop and train deep neural networks (DNNs). This book contains easy-to-follow recipes for leveraging TensorFlow 2.x to develop artificial intelligence applications.Starting with an introduction to the fundamentals of deep reinforcement learning and TensorFlow 2.x, the book covers OpenAI Gym, model-based RL, model-free RL, and how to develop basic agents. You'll discover how to implement advanced deep reinforcement learning algorithms such as actor-critic, deep deterministic policy gradients, deep-Q networks, proximal policy optimization, and deep recurrent Q-networks for training your RL agents. As you advance, you’ll explore the applications of reinforcement learning by building cryptocurrency trading agents, stock/share trading agents, and intelligent agents for automating task completion. Finally, you'll find out how to deploy deep reinforcement learning agents to the cloud and build cross-platform apps using TensorFlow 2.x.By the end of this TensorFlow book, you'll have gained a solid understanding of deep reinforcement learning algorithms and their implementations from scratch.
Jesús Martínez
Computer vision is a scientific field that enables machines to identify and process digital images and videos. This book focuses on independent recipes to help you perform various computer vision tasks using TensorFlow.The book begins by taking you through the basics of deep learning for computer vision, along with covering TensorFlow 2.x’s key features, such as the Keras and tf.data.Dataset APIs. You’ll then learn about the ins and outs of common computer vision tasks, such as image classification, transfer learning, image enhancing and styling, and object detection. The book also covers autoencoders in domains such as inverse image search indexes and image denoising, while offering insights into various architectures used in the recipes, such as convolutional neural networks (CNNs), region-based CNNs (R-CNNs), VGGNet, and You Only Look Once (YOLO). Moving on, you’ll discover tips and tricks to solve any problems faced while building various computer vision applications. Finally, you’ll delve into more advanced topics such as Generative Adversarial Networks (GANs), video processing, and AutoML, concluding with a section focused on techniques to help you boost the performance of your networks.By the end of this TensorFlow book, you’ll be able to confidently tackle a wide range of computer vision problems using TensorFlow 2.x.
Tony Holdroyd
TensorFlow is one of the most popular machine learning frameworks in Python. With this book, you will improve your knowledge of some of the latest TensorFlow features and will be able to perform supervised and unsupervised machine learning and also train neural networks.After giving you an overview of what's new in TensorFlow 2.0 Alpha, the book moves on to setting up your machine learning environment using the TensorFlow library. You will perform popular supervised machine learning tasks using techniques such as linear regression, logistic regression, and clustering. You will get familiar with unsupervised learning for autoencoder applications. The book will also show you how to train effective neural networks using straightforward examples in a variety of different domains.By the end of the book, you will have been exposed to a large variety of machine learning and neural network TensorFlow techniques.
Abhishek Thakur, Alberto Boschetti, Luca Massaron, Alexey...
TensorFlow is one of the most popular frameworks used for machine learning and, more recently, deep learning. It provides a fast and efficient framework for training different kinds of deep learning models, with very high accuracy. This book is your guide to master deep learning with TensorFlow with the help of 10 real-world projects.TensorFlow Deep Learning Projects starts with setting up the right TensorFlow environment for deep learning. You'll learn how to train different types of deep learning models using TensorFlow, including Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, and Generative Adversarial Networks. While doing this, you will build end-to-end deep learning solutions to tackle different real-world problems in image processing, recommendation systems, stock prediction, and building chatbots, to name a few. You will also develop systems that perform machine translation and use reinforcement learning techniques to play games.By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow, and will be able to build and train your own deep learning models with TensorFlow confidently.
Oluwole Fagbohun
The TensorFlow Developer Certificate Guide is an indispensable resource for machine learning enthusiasts and data professionals seeking to master TensorFlow and validate their skills by earning the certification. This practical guide equips you with the skills and knowledge necessary to build robust deep learning models that effectively tackle real-world challenges across diverse industries.You’ll embark on a journey of skill acquisition through easy-to-follow, step-by-step explanations and practical examples, mastering the craft of building sophisticated models using TensorFlow 2.x and overcoming common hurdles such as overfitting and data augmentation. With this book, you’ll discover a wide range of practical applications, including computer vision, natural language processing, and time series prediction.To prepare you for the TensorFlow Developer Certificate exam, it offers comprehensive coverage of exam topics, including image classification, natural language processing (NLP), and time series analysis. With the TensorFlow certification, you’ll be primed to tackle a broad spectrum of business problems and advance your career in the exciting field of machine learning. Whether you are a novice or an experienced developer, this guide will propel you to achieve your aspirations and become a highly skilled TensorFlow professional.
Nick McClure
TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. You’ll work through recipes on training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and deep learning – each using Google’s machine learning library TensorFlow.This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Moving ahead, you will get hands-on experience with Linear Regression techniques with TensorFlow. The next chapters cover important high-level concepts such as neural networks, CNN, RNN, and NLP. Once you are familiar and comfortable with the TensorFlow ecosystem, the last chapter will show you how to take it to production.
Nick McClure
TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and allow you to dig deeper and gain more insights into your data than ever before. With the help of this book, you will work with recipes for training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and more. You will explore RNNs, CNNs, GANs, reinforcement learning, and capsule networks, each using Google's machine learning library, TensorFlow. Through real-world examples, you will get hands-on experience with linear regression techniques with TensorFlow. Once you are familiar and comfortable with the TensorFlow ecosystem, you will be shown how to take it to production.By the end of the book, you will be proficient in the field of machine intelligence using TensorFlow. You will also have good insight into deep learning and be capable of implementing machine learning algorithms in real-world scenarios.
Ankit Jain, Armando Fandango, Amita Kapoor
TensorFlow has transformed the way machine learning is perceived. TensorFlow Machine Learning Projects teaches you how to exploit the benefits—simplicity, efficiency, and flexibility—of using TensorFlow in various real-world projects. With the help of this book, you’ll not only learn how to build advanced projects using different datasets but also be able to tackle common challenges using a range of libraries from the TensorFlow ecosystem.To start with, you’ll get to grips with using TensorFlow for machine learning projects; you’ll explore a wide range of projects using TensorForest and TensorBoard for detecting exoplanets, TensorFlow.js for sentiment analysis, and TensorFlow Lite for digit classification.As you make your way through the book, you’ll build projects in various real-world domains, incorporating natural language processing (NLP), the Gaussian process, autoencoders, recommender systems, and Bayesian neural networks, along with trending areas such as Generative Adversarial Networks (GANs), capsule networks, and reinforcement learning. You’ll learn how to use the TensorFlow on Spark API and GPU-accelerated computing with TensorFlow to detect objects, followed by how to train and develop a recurrent neural network (RNN) model to generate book scripts.By the end of this book, you’ll have gained the required expertise to build full-fledged machine learning projects at work.
Md. Rezaul Karim
Predictive analytics discovers hidden patterns from structured and unstructured data for automated decision making in business intelligence. Predictive decisions are becoming a huge trend worldwide, catering to wide industry sectors by predicting which decisions are more likely to give maximum results. TensorFlow, Google’s brainchild, is immensely popular and extensively used for predictive analysis.This book is a quick learning guide on all the three types of machine learning, that is, supervised, unsupervised, and reinforcement learning with TensorFlow. This book will teach you predictive analytics for high-dimensional and sequence data. In particular, you will learn the linear regression model for regression analysis. You will also learn how to use regression for predicting continuous values. You will learn supervised learning algorithms for predictive analytics. You will explore unsupervised learning and clustering using K-meansYou will then learn how to predict neighborhoods using K-means, and then, see another example of clustering audio clips based on their audio features. This book is ideal 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. This book is embedded with useful assessments that will help you revise the concepts you have learned in this book. This book is repurposed for this specific learning experience from material from Packt's Predictive Analytics with TensorFlow by Md. Rezaul Karim.
Kaushik Balakrishnan
Advances in reinforcement learning algorithms have made it possible to use them for optimal control in several different industrial applications. With this book, you will apply Reinforcement Learning to a range of problems, from computer games to autonomous driving.The book starts by introducing you to essential Reinforcement Learning concepts such as agents, environments, rewards, and advantage functions. You will also master the distinctions between on-policy and off-policy algorithms, as well as model-free and model-based algorithms. You will also learn about several Reinforcement Learning algorithms, such as SARSA, Deep Q-Networks (DQN), Deep Deterministic Policy Gradients (DDPG), Asynchronous Advantage Actor-Critic (A3C), Trust Region Policy Optimization (TRPO), and Proximal Policy Optimization (PPO). The book will also show you how to code these algorithms in TensorFlow and Python and apply them to solve computer games from OpenAI Gym. Finally, you will also learn how to train a car to drive autonomously in the Torcs racing car simulator.By the end of the book, you will be able to design, build, train, and evaluate feed-forward neural networks and convolutional neural networks. You will also have mastered coding state-of-the-art algorithms and also training agents for various control problems.
Teradata Cookbook. Over 85 recipes to implement efficient data warehousing solutions
Abhinav Khandelwal, Rajsekhar Bhamidipati
Teradata is an enterprise software company that develops and sells its eponymous relational database management system (RDBMS), which is considered to be a leading data warehousing solutions and provides data management solutions for analytics. This book will help you get all the practical information you need for the creation and implementation of your data warehousing solution using Teradata.The book begins with recipes on quickly setting up a development environment so you can work with different types of data structuring and manipulation function. You will tackle all problems related to efficient querying, stored procedure searching, and navigation techniques. Additionally, you’ll master various administrative tasks such as user and security management, workload management, high availability, performance tuning, and monitoring.This book is designed to take you through the best practices of performing the real daily tasks of a Teradata DBA, and will help you tackle any problem you might encounter in the process.
Mikael Krief, Mitchell Hashimoto
HashiCorp Configuration Language (HCL) has changed how we define and provision a data center infrastructure with the launch of Terraform—one of the most popular and powerful products for building Infrastructure as Code. This practical guide will show you how to leverage HashiCorp's Terraform tool to manage a complex infrastructure with ease.Starting with recipes for setting up the environment, this book will gradually guide you in configuring, provisioning, collaborating, and building a multi-environment architecture. Unlike other books, you’ll also be able to explore recipes with real-world examples to provision your Azure infrastructure with Terraform. Once you’ve covered topics such as Azure Template, Azure CLI, Terraform configuration, and Terragrunt, you’ll delve into manual and automated testing with Terraform configurations. The next set of chapters will show you how to manage a balanced and efficient infrastructure and create reusable infrastructure with Terraform modules. Finally, you’ll explore the latest DevOps trends such as continuous integration and continuous delivery (CI/CD) and zero-downtime deployments.By the end of this book, you’ll have developed the skills you need to get the most value out of Terraform and manage your infrastructure effectively.
Mikael Krief, Armon Dadgar
HashiCorp Configuration Language (HCL) has changed how we define and provision data center infrastructure with the launch of Terraform, a top-tier product for building Infrastructure as Code (IaC). Terraform Cookbook shows you how to leverage Terraform to manage complex infrastructure with ease.This new edition has been updated to include real-world examples for provisioning Azure, AWS and GCP infrastructure with Terraform. You'll delve into manual and automated testing with Terraform configurations, creating and managing a balanced, efficient, and reusable infrastructure with Terraform modules. You'll learn how to automate the deployment of Terraform configuration with continuous integration and continuous delivery (CI/CD).Besides that, several new chapters have been added that describe the use of Terraform for Docker and Kubernetes, examine advanced topics on GitOps practices, and explain how to test Terraform configurations using different tools to check code and security compliance. The final chapter covers troubleshooting common Terraform issues and provides solutions for frequently encountered errors.By the end of this book, you'll have developed the skills needed to get the most value out of Terraform and to effectively manage your infrastructure.
Bernd Nordhausen
Google Cloud has adopted Terraform as the standard Infrastructure as Code tool. This necessitates a solid understanding of Terraform for any cloud architect or engineer working on Google Cloud. Yet no specific resources are available that focus on how to use Terraform on Google Cloud.This is the first book that teaches Terraform specifically for Google Cloud. You will take a journey from the basic concepts through to deploying complex architectures using Terraform. Using extensive code examples, you will receive guidance on how to authenticate Terraform in Google Cloud. As you advance, you’ll get to grips with all the essential concepts of the Terraform language as applied to Google Cloud and deploy complete working architectures at the push of a button. Finally, you’ll also be able to improve your Terraform workflow using Google Cloud native and third-party tools.By the end of this Terraform book, you will have gained a thorough understanding of Terraform and how to use it on Google Cloud, and be able to develop effective Terraform code, build reusable code, and utilize public domain Terraform modules to deploy on Google Cloud faster and more securely.
Manikandan Sambamurthy
This book helps you build a better understanding of test automation and aids in bridging the gap between testing and test automation.The book has been divided into three sections with the first section focusing on preparing you for testing and test automation fundamentals. By the end of this section, you’ll have an understanding of some common automation terms, definitions, and roles. The second section covers the practical implementation of test automation for mobile, web, API and performance. The third section will help you understand how test automation works with CI/CD, and explore the common issues and pitfalls when executing test automation.By the end of this book, you’ll have a better understanding of automation, addressing the common pain points and best practices around test automation.
Daniel Easterman
Czy Kościół może być zdolny do morderstwa, by zapobiec ujawnieniu antycznego manuskryptu, przewracającego do góry nogami historię świata? W „Testamencie Judasza” znaleźć można wszystko, czego może potrzebować miłośnik powieści sensacyjnej. Skomplikowana fabuła, pełna napięcia i nieoczekiwanych zwrotów akcja obiecują pasjonującą lekturę.
Adelina Simion
Experienced developers understand the importance of designing a comprehensive testing strategy to ensure efficient shipping and maintaining services in production. This book shows you how to utilize test-driven development (TDD), a widely adopted industry practice, for testing your Go apps at different levels. You’ll also explore challenges faced in testing concurrent code, and learn how to leverage generics and write fuzz tests.The book begins by teaching you how to use TDD to tackle various problems, from simple mathematical functions to web apps. You’ll then learn how to structure and run your unit tests using Go’s standard testing library, and explore two popular testing frameworks, Testify and Ginkgo. You’ll also implement test suites using table-driven testing, a popular Go technique. As you advance, you’ll write and run behavior-driven development (BDD) tests using Ginkgo and Godog. Finally, you’ll explore the tricky aspects of implementing and testing TDD in production, such as refactoring your code and testing microservices architecture with contract testing implemented with Pact. All these techniques will be demonstrated using an example REST API, as well as smaller bespoke code examples.By the end of this book, you’ll have learned how to design and implement a comprehensive testing strategy for your Go applications and microservices architecture.