A quick and comprehensive tutorial book for media designers to jump-start interactive multimedia production with computer graphics, digital audio, digital video, and interactivity, using the Pure Data graphical programming environment.An introductory book on multimedia programming for media artists/designers who like to work on interactivity in their projects, digital art/design students who like to learn the first multimedia programming technique, and audio-visual performers who like to customize their performance sets

John Chambers turns his attention to R, the enormously successful open-source system based on the S language. His book guides the reader through programming with R, beginning with simple interactive use and progressing by gradual stages, starting with simple functions. More advanced programming techniques can be added as needed, allowing users to grow into software contributors...(展开全部)

Programming with Types teaches you to design safe, resilient, correct software that’s easy to maintain and understand by taking advantage of the power of strong type systems. Designed to provide practical, instantly-useful techniques for working developers, this clearly-written tutorial introduces you to using type systems to support everyday programming tasks. In it, you’ll ex...(展开全部)

Programming with Types teaches you to design safe, resilient, correct software that’s easy to maintain and understand by taking advantage of the power of strong type systems. Designed to provide practical, instantly-useful techniques for working developers, this clearly-written tutorial introduces you to using type systems to support everyday programming tasks. In it, you’ll ex...(展开全部)

If you want to learn how to use Max 6 and/or TouchDesigner, or work in audio-visual real-time processing, this is the book for you. It is intended for intermediate users of both programs and can be helpful for artists, designers, musicians, VJs, and researchers. A basic understanding of audio principles is advantageous.

This book is for architects and senior managers who are responsible for building a strategy around their current data architecture, helping them identify the need for a Data Lake implementation in an enterprise context. The reader will need a good knowledge of master data management and information lifecycle management, and experience of Big Data technologies.

Budding data scientists and data analysts who are new to the concept of data analysis, or who want to build efficient analytical models in R will find this book to be useful. No prior exposure to data analysis is needed, although a fundamental understanding of the R programming language is required to get the best out of this book.

Data Science with Python begins by introducing you to data science and teaches you to install the packages you need to create a data science coding environment. You will learn three major techniques in machine learning: unsupervised learning, supervised learning, and reinforcement learning. You will also explore basic classification and regression techniques, such as support vector machines, decision trees, and logistic regression.As you make your way through chapters, you will study the basic functions, data structures, and syntax of the Python language that are used to handle large datasets with ease. You will learn about NumPy and pandas libraries for matrix calculations and data manipulation, study how to use Matplotlib to create highly customizable visualizations, and apply the boosting algorithm XGBoost to make predictions. In the concluding chapters, you will explore convolutional neural networks (CNNs), deep learning algorithms used to predict what is in an image. You will also understand how to feed human sentences to a neural network, make the model process contextual information, and create human language processing systems to predict the outcome.By the end of this book, you will be able to understand and implement any new data science algorithm and have the confidence to experiment with tools or libraries other than those covered in the book.

维克托·迈尔—舍恩伯格（Viktor Mayer-Schönberger）牛津大学互联网研究所教授，担任微软、世界经济论坛等大公司和组织的顾问，是大数据领域公认的权威，写过《大数据时代》等八本书和上百篇论文。 肯尼思·库克耶（Kenneth Cukier） 《经济学人》（The Economist）数据编辑，是一位著名的大数据发展评论员。 赵中建 华东师范大学课程与教学研究所教授，上海纽约大学文理学院副院长，国家基础教育课程教材专家工作委员会委员，长期致力于国际教育政策和基础教育研究。 张燕南 上海第二军医大学教师、华东师范大学博士。

Data Analysis with Python offers a modern approach to data analysis so that you can work with the latest and most powerful Python tools, AI techniques, and open source libraries. Industry expert David Taieb shows you how to bridge data science with the power of programming and algorithms in Python. You'll be working with complex algorithms, and cutting-edge AI in your data analysis. Learn how to analyze data with hands-on examples using Python-based tools and Jupyter Notebook. You'll find the right balance of theory and practice, with extensive code files that you can integrate right into your own data projects.Explore the power of this approach to data analysis by then working with it across key industry case studies. Four fascinating and full projects connect you to the most critical data analysis challenges you’re likely to meet in today. The first of these is an image recognition application with TensorFlow – embracing the importance today of AI in your data analysis. The second industry project analyses social media trends, exploring big data issues and AI approaches to natural language processing. The third case study is a financial portfolio analysis application that engages you with time series analysis - pivotal to many data science applications today. The fourth industry use case dives you into graph algorithms and the power of programming in modern data science. You'll wrap up with a thoughtful look at the future of data science and how it will harness the power of algorithms and artificial intelligence.