This new edition of the essential best-practices toolkit shows how to transform an organization into one that continually learns from its experiences, and more importantly, translates that knowledge into improved performance. Rich in hands-on tools, this much-anticipated update of the award-winning classic features lessons from world-class leaders such as Nokia, Alcoa, and othe...(展开全部)

Review "Forget your old, tired ideas about leadership. The most successful corporation of the 1990s will be something called a learning organization." -- Fortune Magazine. Book Description Completely Updated and Revised This revised edition of Peter Senge’s bestselling classic, The Fifth Discipline, is based on fifteen years of experience in putting the book’s ideas into ...(展开全部)

Review "Forget your old, tired ideas about leadership. The most successful corporation of the 1990s will be something called a learning organization." -- Fortune Magazine. Book Description Completely Updated and Revised This revised edition of Peter Senge’s bestselling classic, The Fifth Discipline, is based on fifteen years of experience in putting the book’s ideas into ...(展开全部)

Regarded as one of the most important sociological and business commentaries of modern times, The Organization Man developed the first thorough description of the impact of mass organization on American society. During the height of the Eisenhower administration, corporations appeared to provide a blissful answer to postwar life with the marketing of new technologies-television...(展开全部)

Regarded as one of the most important sociological and business commentaries of modern times, The Organization Man developed the first thorough description of the impact of mass organization on American society. During the height of the Eisenhower administration, corporations appeared to provide a blissful answer to postwar life with the marketing of new technologies-television...(展开全部)

Willi Richert 机器学习和机器人学博士，目前任职于微软Bing搜索核心研发团队。他从事多种机器学习领域的研究，包括主动学习和统计机器翻译。 Luis Pedro Coelho 计算生物学家，主要关注生物图像信息学和大规模图像数据的处理，致力于生物标本图像分析中机器学习技术的应用，他还是Python计算机视觉库mahotas的主要开发人员。他于1998年开始开发开源软件，2004年起从事Python开发，并为多个Python开源库贡献了代码。另外，Luis拥有机器学习领域世界领先的卡内基-梅隆大学的博士学位，并发表过多篇科学论文。

Machine learning allows systems to learn things without being explicitly programmed to do so. Python is one of the most popular languages used to develop machine learning applications, which take advantage of its extensive library support. This third edition of Building Machine Learning Systems with Python addresses recent developments in the field by covering the most-used datasets and libraries to help you build practical machine learning systems.Using machine learning to gain deeper insights from data is a key skill required by modern application developers and analysts alike. Python, being a dynamic language, allows for fast exploration and experimentation. This book shows you exactly how to find patterns in your raw data. You will start by brushing up on your Python machine learning knowledge and being introduced to libraries. You'll quickly get to grips with serious, real-world projects on datasets, using modeling and creating recommendation systems. With Building Machine Learning Systems with Python, you’ll gain the tools and understanding required to build your own systems, all tailored to solve real-world data analysis problems.By the end of this book, you will be able to build machine learning systems using techniques and methodologies such as classification, sentiment analysis, computer vision, reinforcement learning, and neural networks.

Key Features Bored of too much theory on TensorFlow? This book is what you need! Thirteen solid projects and four examples teach you how to implement TensorFlow in production. This example-rich guide teaches you how to perform highly accurate and efficient numerical computing with TensorFlow It is a practical and methodically explained guide that allows you to apply Tensorflow’...(展开全部)

This is a tutorial-driven and practical, but well-grounded book showcasing good Machine Learning practices. There will be an emphasis on using existing technologies instead of showing how to write your own implementations of algorithms. This book is a scenario-based, example-driven tutorial. By the end of the book you will have learnt critical aspects of Machine Learning Python projects and experienced the power of ML-based systems by actually working on them.This book primarily targets Python developers who want to learn about and build Machine Learning into their projects, or who want to provide Machine Learning support to their existing projects, and see them get implemented effectively .Computer science researchers, data scientists, Artificial Intelligence programmers, and statistical programmers would equally gain from this book and would learn about effective implementation through lots of the practical examples discussed.Readers need no prior experience with Machine Learning or statistical processing. Python development experience is assumed.

Key Features Bored of too much theory on TensorFlow? This book is what you need! Thirteen solid projects and four examples teach you how to implement TensorFlow in production. This example-rich guide teaches you how to perform highly accurate and efficient numerical computing with TensorFlow It is a practical and methodically explained guide that allows you to apply Tensorflow’...(展开全部)