Discover best practices, reproducible architectures, and design patterns to help guide deep learning models from the lab into production. In Deep Learning Patterns and Practices you will learn: Internal functioning of modern convolutional neural networks Procedural reuse design pattern for CNN architectures Models for mobile and IoT devices Assembling large-scale model deployments Optimizing hyperparameter tuning Migrating a model to a production environment The big challenge of deep learning lies in taking cutting-edge technologies from R&D labs through to production. Deep Learning Patterns and Practices is here to help. This unique guide lays out the latest deep learning insights from author Andrew Ferlitsch’s work with Google Cloud AI. In it, you'll find deep learning models presented in a unique new way: as extendable design patterns you can easily plug-and-play into your software projects. Each valuable technique is presented in a way that's easy to understand and filled with accessible diagrams and code samples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Discover best practices, design patterns, and reproducible architectures that will guide your deep learning projects from the lab into production. This awesome book collects and illuminates the most relevant insights from a decade of real world deep learning experience. You’ll build your skills and confidence with each interesting example. About the book Deep Learning Patterns and Practices is a deep dive into building successful deep learning applications. You’ll save hours of trial-and-error by applying proven patterns and practices to your own projects. Tested code samples, real-world examples, and a brilliant narrative style make even complex concepts simple and engaging. Along the way, you’ll get tips for deploying, testing, and maintaining your projects. What's inside Modern convolutional neural networks Design pattern for CNN architectures Models for mobile and IoT devices Large-scale model deployments Examples for computer vision About the reader For machine learning engineers familiar with Python and deep learning. About the author Andrew Ferlitsch is an expert on computer vision, deep learning, and operationalizing ML in production at Google Cloud AI Developer Relations. Table of Contents PART 1 DEEP LEARNING FUNDAMENTALS 1 Designing modern machine learning 2 Deep neural networks 3 Convolutional and residual neural networks 4 Training fundamentals PART 2 BASIC DESIGN PATTERN 5 Procedural design pattern 6 Wide convolutional neural networks 7 Alternative connectivity patterns 8 Mobile convolutional neural networks 9 Autoencoders PART 3 WORKING WITH PIPELINES 10 Hyperparameter tuning 11 Transfer learning 12 Data distributions 13 Data pipeline 14 Training and deployment pipeline

The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice. In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation. You'll learn how to: Identify and mitigate common challenges when training, evaluating, and deploying ML models Represent data for different ML model types, including embeddings, feature crosses, and more Choose the right model type for specific problems Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning Deploy scalable ML systems that you can retrain and update to reflect new data Interpret model predictions for stakeholders and ensure models are treating users fairly

Software -- Software Engineering.

In this book, the CEO of Cazton, Inc. and internationally-acclaimed speaker, Chander Dhall, demonstrates current website design scalability patterns and takes a pragmatic approach to explaining their pros and cons to show you how to select the appropriate pattern for your site. He then tests the patterns by deliberately forcing them to fail and exposing potential flaws before discussing how to design the optimal pattern to match your scale requirements. The author explains the use of polyglot programming and how to match the right patterns to your business needs. He also details several No-SQL patterns and explains the fundamentals of different paradigms of No-SQL by showing complementary strategies of using them along with relational databases to achieve the best results. He also teaches how to make the scalability pattern work with a real-world microservices pattern. With the proliferation of countless electronic devices and the ever growing number of Internet users, the scalability of websites has become an increasingly important challenge. Scalability, even though highly coveted, may not be so easy to achieve. Think that you can't attain responsiveness along with scalability? Chander Dhall will demonstrate that, in fact, they go hand in hand. What You'll Learn Architect and develop applications so that they are easy to scale. Learn different scaling and partitioning options and the combinations. Learn techniques to speed up responsiveness. Deep dive into caching, column-family databases, document databases, search engines and RDBMS. Learn scalability and responsiveness concepts that are usually ignored. Effectively balance scalability, performance, responsiveness, and availability while minimizing downtime. Who This Book Is For Executives (CXOs), software architects , developers, and IT Pros

"Mastering Design Patterns in Java: Building Robust and Scalable Software" is your ultimate guide to understanding and implementing design patterns in Java. Whether you're a seasoned developer or just starting your journey with Java, this book equips you with the knowledge and practical skills to tackle software design challenges using well-established, time-tested solutions. Design patterns provide proven approaches to common problems in software design, making code more efficient, reusable, and scalable. This book delves deep into the three main categories of design patterns—Creational, Structural, and Behavioral—offering hands-on examples and practical guidance for each. Patterns such as Singleton, Factory, Adapter, Observer, and many more are explained in detail, with code examples specifically tailored to Java. By the end of each chapter, you’ll not only understand the theoretical underpinnings of each pattern but also know how to apply them effectively in real-world projects. In addition to covering core design patterns, this book takes a step further by addressing advanced topics such as anti-patterns (common pitfalls to avoid), combining patterns in large-scale systems, and using design patterns in cloud-based and microservices architectures. Java developers working on distributed systems, cloud infrastructure, or modern applications will find valuable insights into how design patterns can improve code organization and maintainability. The book’s practical approach ensures that you can immediately start implementing the patterns in your own projects. With exercises, examples, and in-depth explanations, it’s an invaluable resource for any developer looking to improve their software design skills. Whether you're building small applications or architecting large systems, Mastering Design Patterns in Java will help you write clean, modular, and scalable code, positioning you for success in today’s fast-evolving software development landscape. Let this book be your guide to mastering the art of design patterns in Java.

In the ever-evolving landscape of software development, building scalable and robust systems is crucial for success. "Software Architecture Patterns: Designing Scalable and Robust Systems" is a comprehensive guide that explores the key architectural patterns used to create resilient and high-performing software. This book delves into the principles, best practices, and real-world applications of various architectural patterns, providing valuable insights for software architects, developers, and IT professionals. From microservices and event-driven architectures to domain-driven design and serverless computing, this guide offers the tools and knowledge needed to architect systems that meet the demands of modern technology. Unlock the potential of your software with proven patterns and expert guidance.