What is Linux Linux file system Basic commands File permissions Variables Use HPC clusters Processes and jobs File editing

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这个网络研讨会介绍了数据科学的基础知识,并简要回顾了一些统计的基本概念。它还概述了如何拥有一个成功的数据科学项目。

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《A Concise Introduction to Machine Learning》by A.C. Faul (CRC 2019)

关键字

机器学习简介

简介

本书对当下机器学习的发展以及技术进行了简介,循序渐进,深入浅出,适合新手入门。

目录

  • Introduction
  • Probability Theory
  • Sampling
  • Linear Classification
  • Non-Linear Classification
  • Clustering
  • Dimensionality Reduction
  • Regression
  • Feature Learning
  • Appendix A: Matrix Formulae
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In this monograph, I introduce the basic concepts of Online Learning through a modern view of Online Convex Optimization. Here, online learning refers to the framework of regret minimization under worst-case assumptions. I present first-order and second-order algorithms for online learning with convex losses, in Euclidean and non-Euclidean settings. All the algorithms are clearly presented as instantiation of Online Mirror Descent or Follow-The-Regularized-Leader and their variants. Particular attention is given to the issue of tuning the parameters of the algorithms and learning in unbounded domains, through adaptive and parameter-free online learning algorithms. Non-convex losses are dealt through convex surrogate losses and through randomization. The bandit setting is also briefly discussed, touching on the problem of adversarial and stochastic multi-armed bandits. These notes do not require prior knowledge of convex analysis and all the required mathematical tools are rigorously explained. Moreover, all the proofs have been carefully chosen to be as simple and as short as possible.

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简介: 图是表示知识的有效方法。它们可以在一个统一的结构中表示不同类型的知识。生物科学和金融等领域已经开始积累大量的知识图,但是它们缺乏从中提取见解的机器学习工具。

David Mack概述了自己相关想法并调查了最流行的方法。在此过程中,他指出了积极研究的领域,并共享在线资源和参考书目以供进一步研究。

作者介绍: David Mack是Octavian.ai的创始人和机器学习工程师,致力于探索图机器学习的新方法。在此之前,他与他人共同创立了SketchDeck,这是一家由Y Combinator支持的初创公司,提供设计即服务。他拥有牛津大学的数学硕士学位和计算机科学的基础,并拥有剑桥大学的计算机科学学士学位。

内容介绍: 本次报告涵盖内容:为什么将图应用在机器学习上;图机器学习的不同方法。现存的图机器学习往往会忽略数据中的上下文信息,使用图可以获取更多的潜在信息。图的构建方法为节点分类、边的预测,图的分类以及边的分类。两个主要方法是使用机器学习算法将图转换为table,另一种方法是将图转换为网络。在报告中作者详细介绍了这两种方法。

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2019-09 Introduction to graphs and machine learning @ Strata.pdf
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While neural end-to-end text-to-speech (TTS) is superior to conventional statistical methods in many ways, the exposure bias problem in the autoregressive models remains an issue to be resolved. The exposure bias problem arises from the mismatch between the training and inference process, that results in unpredictable performance for out-of-domain test data at run-time. To overcome this, we propose a teacher-student training scheme for Tacotron-based TTS by introducing a distillation loss function in addition to the feature loss function. We first train a Tacotron2-based TTS model by always providing natural speech frames to the decoder, that serves as a teacher model. We then train another Tacotron2-based model as a student model, of which the decoder takes the predicted speech frames as input, similar to how the decoder works during run-time inference. With the distillation loss, the student model learns the output probabilities from the teacher model, that is called knowledge distillation. Experiments show that our proposed training scheme consistently improves the voice quality for out-of-domain test data both in Chinese and English systems.

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Machine-learning models have demonstrated great success in learning complex patterns that enable them to make predictions about unobserved data. In addition to using models for prediction, the ability to interpret what a model has learned is receiving an increasing amount of attention. However, this increased focus has led to considerable confusion about the notion of interpretability. In particular, it is unclear how the wide array of proposed interpretation methods are related, and what common concepts can be used to evaluate them. We aim to address these concerns by defining interpretability in the context of machine learning and introducing the Predictive, Descriptive, Relevant (PDR) framework for discussing interpretations. The PDR framework provides three overarching desiderata for evaluation: predictive accuracy, descriptive accuracy and relevancy, with relevancy judged relative to a human audience. Moreover, to help manage the deluge of interpretation methods, we introduce a categorization of existing techniques into model-based and post-hoc categories, with sub-groups including sparsity, modularity and simulatability. To demonstrate how practitioners can use the PDR framework to evaluate and understand interpretations, we provide numerous real-world examples. These examples highlight the often under-appreciated role played by human audiences in discussions of interpretability. Finally, based on our framework, we discuss limitations of existing methods and directions for future work. We hope that this work will provide a common vocabulary that will make it easier for both practitioners and researchers to discuss and choose from the full range of interpretation methods.

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Character-based neural machine translation (NMT) models alleviate out-of-vocabulary issues, learn morphology, and move us closer to completely end-to-end translation systems. Unfortunately, they are also very brittle and easily falter when presented with noisy data. In this paper, we confront NMT models with synthetic and natural sources of noise. We find that state-of-the-art models fail to translate even moderately noisy texts that humans have no trouble comprehending. We explore two approaches to increase model robustness: structure-invariant word representations and robust training on noisy texts. We find that a model based on a character convolutional neural network is able to simultaneously learn representations robust to multiple kinds of noise.

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