Rediscovering Redis Mastering Data Management Pdf
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Willpower
Can you resist everything except temptation? In a hedonistic age full of distractions, it's hard to possess willpower - or in fact even understand why we should need it. Yet it's actually the most important factor in achieving success and a happy life, shown to be more significant than money, looks, background or intelligence. This book reveals the secrets of self-control. For years the old-fashioned, even Victorian, value of willpower has been disparaged by psychologists who argued that we're largely driven by unconscious forces beyond our control. Here Roy Baumeister, one of the world's most esteemed and influential psychologists, and journalist John Tierney, turn this notion on its head. They show us that willpower is like a muscle that can be strengthened with practice and improved over time. The latest laboratory work shows that self-control has a physical basis to it and so is dramatically affected by simple things such as eating and sleeping - to the extent that a life-changing decision may go in different directions depending on whether it's made before or after lunch. You will discover how babies can be taught willpower, the joys of the to-don't list, the success of Alcoholics Anonymous, the pointlessness of diets and the secrets to David Blaine's stunts. There are also fascinating personal stories, from explorers, students, soldiers, ex-addicts and parents. Based on years of psychological research and filled with practical advice, this book will teach you how to gain from self-control without pain, and discover the very real power in willpower. The results are nothing short of life-changing.
Rediscovering Redis
"Rediscovering Redis: Mastering Data Management" is an essential guide for developers, data engineers, and IT professionals looking to deepen their understanding of Redis, the high-performance in-memory database platform. This comprehensive resource delves into the core principles of Redis, from basic data structures to advanced features like clustering and persistence, offering readers a thorough grounding in Redis's capabilities and applications. The book begins with an introduction to Redis, including its evolution, architecture, and primary use cases, making it accessible to newcomers while providing a solid foundation for more experienced professionals. Subsequent chapters cover a wide range of topics, including configuring Redis for optimal performance, mastering data types, implementing Redis in various programming environments, and leveraging Redis for scalable, high-speed data management solutions. Through practical examples, real-world scenarios, and expert insights, "Rediscovering Redis" equips readers with the skills to design and maintain robust data management solutions. Whether you're developing high-traffic applications, managing large-scale data processing tasks, or seeking to enhance the performance and scalability of your systems, this book offers valuable strategies and best practices for leveraging Redis to its full potential. With its clear explanations, hands-on tutorials, and focus on modern data management challenges, "Rediscovering Redis: Mastering Data Management" is an invaluable resource for anyone looking to master Redis and elevate their data management strategies in the fast-paced world of technology.
Machine Learning Design Patterns
Author: Valliappa Lakshmanan
language: en
Publisher: O'Reilly Media
Release Date: 2020-10-15
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