Eric Jensen是一位非常热爱学习的教师培训人员。他曾是一名教师，执教过从小学到大学的各个层级，目前他正在攻读他的心理学博士学位。他与人共同创立了“超级阵营/数量学习”，是全国第一家且规模最大的脑和谐学习课程，现已有超过50,000名毕业生。

Eric Jensen是一位非常热爱学习的教师培训人员。他曾是一名教师，执教过从小学到大学的各个层级，目前他正在攻读他的心理学博士学位。他与人共同创立了“超级阵营/数量学习”，是全国第一家且规模最大的脑和谐学习课程，现已有超过50,000名毕业生。

If you are a web developer or programmer who wants to create real-life web applications using PHP 7, or a beginner who wants to get started with PHP 7 programming, this book is for you. Prior knowledge of PHP, PHP 7, or programming is not mandatory.

This title is for data scientist and researchers who are already into the field of data science and want to see machine learning in action and explore its real-world application. Prior knowledge of Python programming and mathematics is must with basic knowledge of machine learning concepts.

《高效学习:我们所知道的理解性教学》是美国斯坦福大学教授、奥巴马政府现行教育政策改革团队领导人琳达·达林-哈蒙德(Linda Darling-Hammond)与布里基·巴伦(Brigid Barron)等7位知名学者花了十多年的时间开展学习研究的成果。他们基于人是如何学习的理论观点，聚焦于阅读与素养、数学、科学这三大主要学科领域，从理论和实践层面探索了“理解性教学”的教学形式(基于项目学习、基于设计的学习和基于问题的学习)，阐释了在新技术的强大支持下，革新课堂学习环境，促进高效学习的策略以及影响这些策略有效性的因素与条件。他们还提出了变革学校体系，为理解性教学建立学校的主张。 In Powerful Learning, Linda Darling-Hammond and an impressive list of co-authors offer a...(展开全部)

《高效学习:我们所知道的理解性教学》是美国斯坦福大学教授、奥巴马政府现行教育政策改革团队领导人琳达·达林-哈蒙德(Linda Darling-Hammond)与布里基·巴伦(Brigid Barron)等7位知名学者花了十多年的时间开展学习研究的成果。他们基于人是如何学习的理论观点，聚焦于阅读与素养、数学、科学这三大主要学科领域，从理论和实践层面探索了“理解性教学”的教学形式(基于项目学习、基于设计的学习和基于问题的学习)，阐释了在新技术的强大支持下，革新课堂学习环境，促进高效学习的策略以及影响这些策略有效性的因素与条件。他们还提出了变革学校体系，为理解性教学建立学校的主张。 In Powerful Learning, Linda Darling-Hammond and an impressive list of co-authors offer a...(展开全部)

Deep learning is changing everything. This machine-learning method has already surpassed traditional computer vision techniques, and the same is happening with NLP. If you're looking to bring deep learning into your domain, this practical book will bring you up to speed on key concepts using Facebook's PyTorch framework. Once author Ian Pointer helps you set up PyTorch on a clo...(展开全部)

Get started with Ray, the open source distributed computing framework that greatly simplifies the process of scaling compute-intensive Python workloads. With this practical book, Python programmers, data engineers, and data scientists will learn how to leverage Ray locally and spin up compute clusters. You'll be able to use Ray to structure and run machine learning programs at ...(展开全部)

Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including in insurance, transactions, and lending. It explains the concepts and algorithms behind the main machine learning techniques and provides example Python code for implementing the models yourself.The book is based on Jannes Klaas’ experience of running machine learning training courses for financial professionals. Rather than providing ready-made financial algorithms, the book focuses on the advanced ML concepts and ideas that can be applied in a wide variety of ways.The book shows how machine learning works on structured data, text, images, and time series. It includes coverage of generative adversarial learning, reinforcement learning, debugging, and launching machine learning products. It discusses how to fight bias in machine learning and ends with an exploration of Bayesian inference and probabilistic programming.

Machine learning presents an entirely unique opportunity in software development. It allows smartphones to produce an enormous amount of useful data that can be mined, analyzed, and used to make predictions. This book will help you master machine learning for mobile devices with easy-to-follow, practical examples.You will begin with an introduction to machine learning on mobiles and grasp the fundamentals so you become well-acquainted with the subject. You will master supervised and unsupervised learning algorithms, and then learn how to build a machine learning model using mobile-based libraries such as Core ML, TensorFlow Lite, ML Kit, and Fritz on Android and iOS platforms. In doing so, you will also tackle some common and not-so-common machine learning problems with regard to Computer Vision and other real-world domains.By the end of this book, you will have explored machine learning in depth and implemented on-device machine learning with ease, thereby gaining a thorough understanding of how to run, create, and build real-time machine-learning applications on your mobile devices.