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)

We're living in a big data world. Being able to make near-real-time decisions becomes increasingly crucial. To succeed, we need machine learning systems that can turn massive amounts of data into valuable insights. But when you're just starting out in the data science field, how do you get started creating machine learning applications? The answer is TensorFlow, a new open sour...(展开全部)

This fully revised second edition of Machine Learning with TensorFlow teaches you the foundational concepts of machine learning, and how to utilize the TensorFlow library to rapidly build powerful ML models. You’ll learn the basics of regression, classification, and clustering algorithms, applying them to solve real-world challenges such as call center volume prediction and sen...(展开全部)

This book is for data scientists, machine learning developers as well as deep learning practitioners, who want to build interesting deep learning projects that leverage the power of Tensorflow. Some understanding of machine learning and deep learning, and familiarity with the TensorFlow framework is all you need to get started with this book.

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.

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。

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.

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。

If you want to leverage the power of both Scala and Spark to make sense of Big Data, then this book is for you. If you are well versed with machine learning concepts and wants to expand your knowledge by delving into the practical implementation using the power of Scala, then this book is what you need! Strong understanding of Scala Programming language is recommended. Basic familiarity with machine Learning techniques will be more helpful.

R is one of the most popular languages when it comes to performing computational statistics (statistical computing) easily and exploring the mathematical side of machine learning. With this book, you will leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization.This book will help you test your knowledge and skills, guiding you on how to build easily through to complex machine learning projects. You will first learn how to build powerful machine learning models with ensembles to predict employee attrition. Next, you’ll implement a joke recommendation engine and learn how to perform sentiment analysis on Amazon reviews. You’ll also explore different clustering techniques to segment customers using wholesale data. In addition to this, the book will get you acquainted with credit card fraud detection using autoencoders, and reinforcement learning to make predictions and win on a casino slot machine.By the end of the book, you will be equipped to confidently perform complex tasks to build research and commercial projects for automated operations.