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2018 Modern text analysis is now very accessible using Python and open source tools, so discover how you can now perform modern text analysis in this era of textual data. This book shows you how to use natural language processing, and computational linguistics algorithms, to make inferences and gain insights about data you have. These algorithms are based on statistical machine learning and artificial intelligence techniques. The tools to work with these algorithms are available to you right now - with Python, and tools like Gensim and spaCy. You'll start by learning about data cleaning, and then how to perform computational linguistics from first concepts. You're then ready to explore the more sophisticated areas of statistical NLP and deep learning using Python, with realistic language and text samples. You'll learn to tag, parse, and model text using the best tools. You'll gain hands-on knowledge of the best frameworks to use, and you'll know when to choose a tool like Gensim for topic models, and when to work with Keras for deep learning. This book balances theory and practical hands-on examples, so you can learn about and conduct your own natural language processing projects and computational linguistics. You'll discover the rich ecosystem of Python tools you have available to conduct NLP - and enter the interesting world of modern text analysis. What you will learn Why text analysis is important in our modern age Understand NLP terminology and get to know the Python tools and datasets Learn how to pre-process and clean textual data Convert textual data into vector space representations Using spaCy to process text Train your own NLP models for computational linguistics Use statistical learning and Topic Modeling algorithms for text, using Gensim and scikit-learn Employ deep learning techniques for text analysis using Keras

This Report is an expansion of a previous Report on the DARPA XAI Program, which was titled "Literature Review and Integration of Key Ideas for Explainable AI," and was dated February 2018. This new version integrates nearly 200 additional references that have been discovered. This Report includes a new section titled "Review of Human Evaluation of XAI Systems." This section focuses on reports—many of them recent—on projects in which human-machine AI or XAI systems underwent some sort of empirical evaluation. This new section is particularly relevant to the empirical and experimental activities in the DARPA XAI Program

提出了一种新颖的连接文本提议网络（CTPN），它能够准确定位自然图像中的文本行。CTPN直接在卷积特征映射中的一系列细粒度文本提议中检测文本行。我们开发了一个垂直锚点机制，联合预测每个固定宽度提议的位置和文本/非文本分数，大大提高了定位精度。序列提议通过循环神经网络自然地连接起来，该网络无缝地结合到卷积网络中，从而形成端到端的可训练模型。这使得CTPN可以探索丰富的图像上下文信息，使其能够检测极其模糊的文本。CTPN在多尺度和多语言文本上可靠地工作，而不需要进一步的后处理，脱离了以前的自底向上需要多步后过滤的方法。 作者：SnailTyan 链接：https://www.jianshu.com/p/7c55cbd4a68f 來源：简书 简书著作权归作者所有，任何形式的转载都请联系作者获得授权并注明出处。

Foundations of Data Science∗ Avrim Blum, John Hopcroft and Ravindran Kannan Thursday 9th June, 2016 美国学校的教材，系统讲述机器学习、深度学习的数学问题 目录： 2 High-Dimensional Space 3 Best-Fit Subspaces and Singular Value Decomposition (SVD) 4 Random Graphs 5 Random Walks and Markov Chains 6 Machine Learning 7 Algorithms for Massive Data Problems: Streaming, Sketching, and Sampling 8 Clustering 9 Topic Models, Hidden Markov Process, Graphical Models, and Belief Propagation 10 Other Topics 11 Wavelets 12 Appendix

《生命3.0》一书中，作者迈克斯•泰格马克对人类的终极未来进行了全方位的畅想，从我们能活到的近未来穿行至1万年乃至10 亿年及其以后，从可见的智能潜入不可见的意识，重新定义了“生命”“智能”“目标”“意识”，并澄清了常见的对人工智能的误解，将帮你构建起应对人工智能时代动态的全新思维框架，抓住人类与人工智能共生演化的焦点。

作者：Richard S. Sutton and Andrew G. Barto The idea that we learn by interacting with our environment is probably the rst to occur to us when we think about the nature of learning. When an infant plays, waves its arms, or looks about, it has no explicit teacher, but it does have a direct sensorimotor connection to its environment. Exercising this connection produces a wealth of information about cause and eect, about the consequences of actions, and about what to do in order to achieve goals. Throughout our lives, such interactions are undoubtedly a major source of knowledge about our environment and ourselves. Whether we are learning to drive a car or to hold a conversation, we are acutely aware of how our environment responds to what we do, and we seek to in uence what happens through our behavior. Learning from interaction is a foundational idea underlying nearly all theories of learning and intelligence.

第一，本文提出基于观测路径相似性的粒子估计算法。 第二，考虑到观测时间间隔较长时，平滑操作对上述基于观测路径相似 性的粒子估计算法的实时性影响较大，本文提出基于观测路径相似性重采样 的粒子滤波算法。 第三，针对在粒子退化严重使所有粒子权值都等于零的情况下，现有粒 子滤波算法无法继续进行滤波，提出了先判断各粒子似然函数值是否全为零 并根据判断结果决定后续执行步骤的改进策略。

本书已经被使用了近30年，至今仍被欧美许多著名高校所广泛使用。本书提供了一个理解现有方法和技术以及为以后的研究做准备的系统框架，其中包含了很多将机器视觉方法应用于实际问题的内容。全书共包括18章，前13章主要讲述早期视觉的内容，后5章更加关注于：解决一些更加复杂的实际问题。

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词汇和语言结构，汉语语言变异研究，计算词汇学，汉语素论、类论、搭配论