blackkettle的专栏

The document addresses an issue that has been qualified as “challenging” in the literature, that is, estimating the parameters of the well-known Pareto/NBD model. The authors propose a maximum likelihood method to compute the model parameters and illustrate it with two data sets that have been previously used in the literature. The proposed procedure leads to the same parameter estimates as previously published approaches and can be implemented in a user-friendly fashion using a spreadsheet.

This book presents the implementation of seven practical, real-world projects that will teach you how to leverage TensorFlow Lite and Core ML to perform efficient machine learning. We will be learning about the recent advancements in TensorFlow and its extensions, such as TensorFlow Lite, to design intelligent apps that learn from complex/large datasets. We will delve into advancements such as deep learning by building apps using deep neural network architecture such as Convolutional Neural Networks (CNNs), recurrent neural networks (RNNs), transfer learning, and much more.

本教程旨在重点介绍在Tensorflow中构建word2vec模型时的一些有趣且重要的部分。 1.我们将先说明将字词表示为向量的动机 2.我们会介绍模型的原理及其训练方式 3.我们还会在Tensorflow中展示模型的简单实现 4.我们会介绍如何提高该简单版本的扩展性能

It has been a big year for the field of natural language processing (NLP) and for machine learning as a whole. There have been many trends and breaking stories, with state-ofthe-art results and new interesting research directions emerging. We owe all of this progress to the brilliant researchers around the world and the millions of developers devoting their full time to improve tools that make it easier for everyone to learn and progress the field. We have witnessed a rise in transfer learning and other niche areas of research such as AI Ethics

这本书是讲数据科学理论的，是北大教授姚远写的，难度较大。This monograph aims to provide graduate students or senior graduates in applied mathematics, computer science and statistics an introduction to data science from a mathematical perspective. It is focused around a central topic in data analysis, Principal Component Analysis (PCA), with a divergence to some mathematical theories for deeper understanding, such as random matrix theory, convex optimization, random walks on graphs, geometric and topological perspectives in data analysis.

Apache Spark is a fast, scalable, and flexible open source distributed processing engine for big data systems and is one of the most active open source big data projects to date. In just 24 lessons of one hour or less, Sams Teach Yourself Apache Spark in 24 Hours helps you build practical Big Data solutions that leverage Spark’s amazing speed, scalability, simplicity, and versatility. This book’s straightforward, step-by-step approach shows you how to deploy, program, optimize, manage, integrate, and extend Spark–now, and for years to come. You’ll discover how to create powerful solutions encompassing cloud computing, real-time stream processing, machine learning, and more. Every lesson builds on what you’ve already learned, giving you a rock-solid foundation for real-world success. Whether you are a data analyst, data engineer, data scientist, or data steward, learning Spark will help you to advance your career or embark on a new career in the booming area of Big Data.

Data processing, implementing related algorithms, tuning, scaling up and finally deploying are some crucial steps in the process of optimising any application. Spark is capable of handling large-scale batch and streaming data to figure out when to cache data in memory and processing them up to 100 times faster than Hadoop-based MapReduce.This means predictive analytics can be applied to streaming and batch to develop complete machine learning (ML) applications a lot quicker, making Spark an ideal candidate for large data-intensive applications. This book focuses on design engineering and scalable solutions using ML with Spark. First, you will learn how to install Spark with all new features from the latest Spark 2.0 release. Moving on, you’ll explore important concepts such as advanced feature engineering with RDD and Datasets. After studying developing and deploying applications, you will see how to use external libraries with Spark.

这个例子是一个关于 DLL 调用 MessageBox。其中代码还有要改正的地方。

Hadoop got its start in Nutch. A few of us were attempting to build an open source web search engine and having trouble managing computations running on even a handful of computers. Once Google published its GFS and MapReduce papers, the route became clear. They’d devised systems to solve precisely the problems we were having with Nutch. So we started, two of us, half-time, to try to recreate these systems as a part of Nutch.

It is a book about how to use SAS Enterprise Miner for data mining.

本书是专门为Oracle应用开发人员所提供的编程指南。它不仅为应用开发人员提供了在PL/SQL块中嵌入SQL语句和编写子程序（过程，函数和包）的方法，而且还介绍了在 PL/SQL块中使用记录变量（类似于C/C++的结构），集合类型（索引表，嵌套表，VARRAY-类似于C/C++的数组），对象类型（类似于C++/Java的类）和处理LOB对象 的方法，为应用开发人员提供了大量 Oracle9i 和 Oracle10g新增加的PL/SQL特征。