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Open-book management is considered to be a success when companies allow improvements on their financial numbers to come from the bottom tier of employee rather than pressure exerted by a traditional top-down management system.
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However taking a company from "normal" to open is not as easy as just sharing financial statements with employees. In a company fully employing open-book management employees at all levels are very knowledgeable about how their job fits into the financial plan for the company.
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( August 2017) ( Learn how and when to remove this template message) Please help improve it by removing promotional content and inappropriate external links, and by adding encyclopedic content written from a neutral point of view. This book is the perfect way to step into an exciting-and lucrative-career as a deep learning engineer.This article contains content that is written like an advertisement.
#Open source ebook manager how to
You’ll learn how to build automated and scalable services for core tasks like dataset management, model training/serving, and hyperparameter tuning.
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It’s full of hands-on examples that will help you transfer your software development skills to implementing deep learning platforms. Pick the right open-source project for your platformĭesigning Deep Learning Systems is a practical guide for software engineers and data scientists who are designing and building platforms for deep learning.Optimize dataset management, training, model serving and hyperparameter tuning.Automate training for models in TensorFlow and PyTorch.Understand the deep learning development cycle.Recognize and solve common engineering challenges for deep learning systems.Transfer your software development skills to deep learning systems.In Designing Deep Learning Systems you will learn how to: Written for software engineers, this book teaches you how to implement a maintainable platform for developing deep learning models. Design systems optimized for deep learning models.
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