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Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography. The book introduces theory in tandem with applications. Information theory is taught alongside practical communication systems such as arithmetic coding for data compression and sparse-graph codes for error-correction. Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks. Uniquely, the book covers state-of-the-art error-correcting codes, including low-density-parity-check codes, turbo codes, and digital fountain codes - the twenty-first-century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, the book is ideal for self-learning, and for undergraduate or graduate courses. It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering and machine learning.

本书将教你充分利用集群硬件优势的架构，以及专门设计用来捕获和分析网络规模数据的新工具，来创建这些系统。其中描述了一个可扩展的、易于理解大数据系统的方法，可以由小团队构建并运行；并利用一个实际示例，基于大数据系统的理论在实践中实现它们来指导读者。本书共18章。第1章介绍了数据系统的原理，并对Lambda架构进行了概述；第2章到第9章集中阐述了Lambda架构的批处理层；第10章和第11章讲述服务层的内容；第12章到17章讲述速度层的内容；第18章再次巩固Lambda架构的相关知识，并进行查漏补缺。

具体论文名列表： A deep learning framework for financial time series using stacked autoencoders and longshort term memory.pdf Big_Data_Deep_Learning_for_financial_sentiment_ana.pdf Deep Direct Reinforcement Learning for Financial Signal Representation and Trading.pdf Deep Leaming for Stock Market Prediction Using Technical Indicators and Financial.pdf Deep learning for finance deep portfolios.pdf Deep Learning for Multivariate Financial Time Series.pdf Deep Learning for Stock Prediction Using Numerical and Textual Information.pdf Deep Learning in Finance.pdf Deep Learning Networks for Stock Market Analysis and Prediction.pdf Deep Learning Stock Volatility with Google Domestic Trends.pdf deep stock representation learning from candlestick charts to investment decisions.pdf Financial Analysis Stock Market Prediction Using Deep Learning Algorithms.pdf Knowledge-Driven Stock Trend Prediction and Explanation via Temporal Convolutional Network.pdf Leveraging Financial News for Stock Trend Prediction with Attention-Based Recurrent Neural Network.pdf list.txt Machine Learning Techniques for Stock Prediction.pdf Stock Price Prediction Using Machine Learning and Deep Learning Frameworks.pdf 变步长BLSTM集成学习股票预测.pdf 论文阅读笔记Deep learning for event-driven stock prediction.pdf

We offer a systematic analysis of the use of deep learning networks for stock market analysis and prediction. Its ability to extract features from a large set of raw data without relying on prior knowledge of predictors makes deep learning potentially attractive for stock market prediction at high frequencies. Deep learning algorithms vary considerably in the choice of network structure, activation function, and other model parameters, and their performance is known to depend heavily on the method of data representation. Our study attempts to provides a comprehensive and objective assessment of both the advantages and drawbacks of deep learning algorithms for stock market analysis and prediction. Using high-frequency intraday stock returns as input data, we examine the effects of three unsupervised feature extraction methods—principal component analysis, autoencoder, and the restricted Boltzmann machine—on the network’s overall ability to predict future market behavior. Empirical results suggest that deep neural networks can extract additional information from the residuals of the autoregressive model and improve prediction performance; the same cannot be said when the autoregressive model is applied to the residuals of the network. Covariance estimation is also noticeably improved when the predictive network is applied to covariance-based market structure analysis. Our study offers practical insights and potentially useful directions for further investigation into how deep learning networks can be effectively used for stock market analysis and prediction.

作者：拉结帝普·杜瓦 ，曼普利特·辛格·古特拉， 尼克·彭特里思 译者：蔡立宇，黄章帅，周济民 本书结合案例研究讲解Spark 在机器学习中的应用，并介绍如何从各种公开渠道获取用于机器学习系统的数据。 书中内容涵盖推荐系统、回归、聚类、降维等经典机器学习算法及其实际应用。 第2版新增了有关机器学习数学基础以及Spark ML Pipeline API 的章节，内容更加系统、全面、与时俱进。

全栈数据很好的入门书籍，高清带书签。以数据分析领域最热的Python语言为主要线索，介绍了数据分析库numpy、Pandas与机器学习库scikit-learn，使用了可视化环境Orange 3来理解算法的一些细节。对于机器学习，既有常用算法kNN与Kmeans的应用，决策树与随机森林的实战，还涉及常用特征工程与深度学习中的自动编程器。在大数据Hadoop与Hive环境的基础之上，使用Spark的ML/MLlib库集成了前面的各部分内容，让分布式机器学习更容易。大量的工具与技能实战的介绍将各部分融合成一个全栈的数据科学内容。