Python Problem Sets

With the development and popularity of artificial intelligence technology, Python has surpassed many other programming languages and has become one of the most popular and most commonly used programming languages in the field of machine learning.

C + + computer vision ccv-based on C language/provides cache/core machine Vision Library, novel Machine Vision Library opencv-It provides C + +, C, Python, Java and MATLAB interfaces, and supports Windows, Linux, Android and Mac OS operating system. General machine learning Mlpack dlib Ecogg Shark Closure Universal machine learning Closure Toolbox-cloj ...

Open source machine learning tools also allow you to migrate learning, which means you can solve machine learning problems by applying other aspects of knowledge.

Spark can read and write data directly to HDFS and also supports Spark on YARN. Spark runs in the same cluster as MapReduce, shares storage resources and calculations, borrows Hive from the data warehouse Shark implementation, and is almost completely compatible with Hive. Spark's core concepts 1, Resilient Distributed Dataset (RDD) flexible distribution data set RDD is ...

Introduction: It is well known that R is unparalleled in solving statistical problems. But R is slow at data speeds up to 2G, creating a solution that runs distributed algorithms in conjunction with Hadoop, but is there a team that uses solutions like python + Hadoop? R Such origins in the statistical computer package and Hadoop combination will not be a problem? The answer from the king of Frank: Because they do not understand the characteristics of R and Hadoop application scenarios, just ...

In mailbox rapid expansion process, one of the performance problems is the MongoDB database level write lock, the time spent in the lock waiting process, directly reflects the user's use of the service process delay. To address this long-standing problem, we decided to migrate a common set of MongoDB (storing mail-related data) to a separate cluster. According to our inference, this will reduce the lock latency by 50%, and we can add more fragments, and we expect to be able to optimize and manage different types of data independently. We start from Mon ...

In machine learning applications, privacy should be considered an ally, not an enemy. With the improvement of technology. Differential privacy is likely to be an effective regularization tool that produces a better behavioral model. For machine learning researchers, even if they don't understand the knowledge of privacy protection, they can protect the training data in machine learning through the PATE framework.

Machine learning uses algorithms to extract information from raw data and present it in some type of model. We use this model to infer other data that has not been modeled.

To use Hadoop, data consolidation is critical and hbase is widely used. In general, you need to transfer data from existing types of databases or data files to HBase for different scenario patterns. The common approach is to use the Put method in the HBase API, to use the HBase Bulk Load tool, and to use a custom mapreduce job. The book "HBase Administration Cookbook" has a detailed description of these three ways, by Imp ...

Here is a translation of the Redis Official document "A fifteen minute introduction to Redis data Types", as the title says, The purpose of this article is to allow a beginner to have an understanding of the Redis data structure through 15 minutes of simple learning. Redis is a kind of "key/value" type data distributed NoSQL database system, characterized by high-performance, persistent storage, to adapt to high concurrent application scenarios. It started late, developed rapidly, has been many ...