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  • TensorFlow机器学习实战指南 Machine Learning Cookbook

    Chapter 1, Getting Started with TensorFlow, covers the main objects and concepts in TensorFlow. We introduce tensors, variables, and placeholders. We also show how to work with matrices and various mathematical operations in TensorFlow. At the end of the chapter we show how to access the data sources used in the rest of the book. Chapter 2, The TensorFlow Way, establishes how to connect all the algorithm components from Chapter 1 into a computational graph in multiple ways to create a simple classifier. Along the way, we cover computational graphs, loss functions, back propagation, and training with data. Chapter 3, Linear Regression, focuses on using TensorFlow for exploring various linear regression techniques, such as Deming, lasso, ridge, elastic net, and logistic regression. We show how to implement each in a TensorFlow computational graph. Chapter 4, Support Vector Machines, introduces support vector machines (SVMs) and shows how to use TensorFlow to implement linear SVMs, non-linear SVMs, and multi-class SVMs. Chapter 5, Nearest Neighbor Methods, shows how to implement nearest neighbor techniques using numerical metrics, text metrics, and scaled distance functions. We use nearest neighbor techniques to perform record matching among addresses and to classify hand-written digits from the MNIST database. Chapter 6, Neural Networks, covers how to implement neural networks in TensorFlow, starting with the operational gates and activation function concepts. We then show a shallow neural network and show how to build up various different types of layers. We end the chapter by teaching TensorFlow to play tic-tac-toe via a neural network method. Chapter 7, Natural Language Processing, illustrates various text processing techniques with TensorFlow. We show how to implement the bag-of-words technique and TF-IDF for text. We then introduce neural network text representations with CBOW and skip-gram and use these techniques for Word2Vec and Doc2Vec for making real-world predictions. Chapter 8, Convolutional Neural Networks, expands our knowledge of neural networks by illustrating how to use neural networks on images with convolutional neural networks (CNNs). We show how to build a simple CNN for MNIST digit recognition and extend it to color images in the CIFAR-10 task. We also illustrate how to extend prior trained image recognition models for custom tasks. We end the chapter by explaining and showing the stylenet/neural style and deep-dream algorithms in TensorFlow. Chapter 9, Recurrent Neural Networks, explains how to implement recurrent neural networks (RNNs) in TensorFlow. We show how to do text-spam prediction, and expand the RNN model to do text generation based on Shakespeare. We also train a sequence to sequence model for German-English translation. We finish the chapter by showing the usage of Siamese RNN networks for record matching on addresses. Chapter 10, Taking TensorFlow to Production, gives tips and examples on moving TensorFlow to a production environment and how to take advantage of multiple processing devices (for example GPUs) and setting up TensorFlow distributed on multiple machines. Chapter 11, More with TensorFlow, show the versatility of TensorFlow by illustrating how to do k-means, genetic algorithms, and solve a system of ordinary differential equations (ODEs). We also show the various uses of Tensorboard, and how to view computational graph metrics.

    2018-04-26
    2
  • 2018年人工智能标准化白皮书

    本白皮书前期在国标委工业二部和工信部科技司的指导下,通过梳理人工智 能技术、应用和产业演进情况,分析人工智能的技术热点、行业动态和未来趋势, 从支撑人工智能产业整体发展的角度出发,研究制定了能够适应和引导人工智能 产业发展的标准体系,进而提出近期急需研制的基础和关键标准项目。

    2018-01-20
    5
  • Data Science for Business

    Review, 'A must-read resource for anyone who is serious about embracing the opportunity of big data.', -- Craig Vaughan, Global Vice President at SAP, 'This book goes beyond data analytics 101. It's the essential guide for those of us (all of us?) whose businesses are built on the ubiquity of data opportunities and the new mandate for data-driven decision-making.', --Tom Phillips, CEO of Media6Degrees and Former Head of Google Search and Analytics, 'Data is the foundation of new waves of productivity growth, innovation, and richer customer insight. Only recently viewed broadly as a source of competitive advantage, dealing well with data is rapidly becoming table stakes to stay in the game. The authors' deep applied experience makes this a must read--a window into your competitor's strategy.', -- Alan Murray, Serial Entrepreneur; Partner at Coriolis Ventures, 'This timely book says out loud what has finally become apparent: in the modern world, Data is Business, and you can no longer think business without thinking data. Read this book and you will understand the Science behind thinking data.', -- Ron Bekkerman, Chief Data Officer at Carmel Ventures, 'A great book for business managers who lead or interact with data scientists, who wish to better understand the principles and algorithms available without the technical details of single-disciplinary books.', -- Ronny Kohavi, Partner Architect at Microsoft Online Services Division, About the Author, Foster Provost is Professor and NEC Faculty Fellow at the NYU Stern School of Business where he teaches in the MBA, Business Analytics, and Data Science programs. His award-winning research is read and cited broadly. Prof. Provost has co-founded several successful companies focusing on data science for marketing., Tom Fawcett holds a Ph.D. in machine learning and has worked in industry R&D for more than two decades for companies such as GTE Laboratories, NYNEX/Verizon Labs, and HP Labs. His published work has become standard reading in data science.

    2017-11-26
    7
  • 集成方法进行模式分类(Pattern Classification Using Ensemble Methods)

    Ensemble methodology imitates our second nature to seek several opinions before making a crucial decision. The core principle is to weigh several individual pattern classifiers, and combine them in order to reach a classification that is better than the one obtained by each of them separately. Researchers from various disciplines such as pattern recognition, statistics, and machine learning have explored the use of ensemble methods since the late seventies. Given the growing interest in the field, it is not surprising that researchers and practitioners have a wide variety of methods at their disposal. Pattern Classification Using Ensemble Methods aims to provide a methodic and well structured introduction into this world by presenting a coherent and unified repository of ensemble methods, theories, trends, challenges and applications. Its informative, factual pages will provide researchers, students and practitioners in industry with a comprehensive, yet concise and convenient reference source to ensemble methods. The book describes in detail the classical methods, as well as extensions and novel approaches that were recently introduced. Along with algorithmic descriptions of each method, the reader is provided with a description of the settings in which this method is applicable and with the consequences and the trade-offs incurred by using the method. This book is dedicated entirely to the field of ensemble methods and covers all aspects of this important and fascinating methodology.

    2013-07-13
    12
  • Parallel Port Complete

    Parallel Port Complete Programming, Interfacing, & Using the PC’s Parallel Printer Port by Jan Axelson

    2010-03-21
    2
  • parallel port complete

    Parallel Port Complete Programming, Interfacing, & Using the PC’s Parallel Printer Port by Jan Axelson

    2010-03-21
    3
  • Cpu Control

    CPU-Control software will handle the CPU-affinity for multicore-systems CPU-Control software will handle the CPU-affinity for multicore-systems. Instead of running each process on both CPUs you can define it as you want it. For example , if you want to seperate the firewall and the anti-virus-software from the graphics-application. CPU-Controls offers five different ways to control the CPU-affinity:Automatic: It chooses alternatingly one CPU for each new processManual: You define a list, where you can set the way to handle each processAll processes run on CPU 1, which is useful for old applications which crashes on a dual core systemAll processes run on CPU 2Deactivated. . Koma-Code Software, Koma-Mail, Yiola, CPU control, dual core

    2010-03-21
    3
  • Mathematics and Visualization

    This book is based on selected lectures given by leading experts in Scientific Visualization during a workshop held at Schloss Dagstuhl, Germany. Topics include user issues in visualization, large data visualization, unstructured mesh processing for visualization, volumetric visualization, flow visualization, medical visualization and visualization systems. The methods of visualizing data developed by Scientific Visualization researchers presented in this book are having broad impact on the way other scientists, engineers and practitioners are processing and understanding their data from sensors, simulations and mathematics models.

    2010-03-19
    5
  • Computer Visualization - Graphics Techniques for Engineering and Scientific Analysis

    Computer Visualization - Graphics Techniques for Engineering and Scientific Analysis

    2010-03-19
    5
  • Knapsack Problems. Algorithms and Computer Implementations

    Knapsack Problems. Algorithms and Computer Implementations

    2010-03-19
    9
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