kaggle datasets

Several novice programmers won the Kaggle Predictive modeling contest after enrolling for a few days of "machine learning" courses on Coursera for free. The big data talent scare that the industry has made in it--McKinsey is the initiator--has raised expectations and demands for big data and advanced analytics talent, and data scientists have become the sexiest career of the night, with its halo chasing sports stars. Data scientists are portrayed as G

development of better algorithms through events suchAs this, it is now not only quite feasible but rapidly becoming a way of doing business in production industries.This event, the musicData Science hackathon, is clear evidence of that because it involved the music giant EMI MusicSharing its highly prized EMI millionInterview dataset for the very first time. this is a vast and uniquely rich dataset compiled from 20-minute interviews with 800,000 music lovers from 25 different countries, recordi

. Download the image from your hub.docker.com:1. Docker Login2. Docker Pull Xz295139210/python:v2 Search Image:[Root@localhost ~]# Docker Search pythonINDEX NAME DESCRIPTION STARS Official automatedDocker.io Docker.io/python Python is a interpreted, interactive, obj ... 1621 [OK]Docker.io Docker.io/kaggle/python Docker image for Python scripts run on Kaggle [OK] Naming format: Docker.io/

is a huge regression 11. As colleagues, they have made a series of arguments that the row-oriented parallel database is better than hadoop's benchmark test [120,144]. However, let's look at Dean's and Ghemawat's counterargument [47] and the recently attempted hybrid architecture [1]. We must stop discussing this dynamic thing, instead of focusing on algorithms. From the perspective of an application, it is very likely that a data warehouse-based data analysis does not need to write mapreduce p

data sets should preferably be sorted beforehand in order to improve retrieval efficiency.If the datasets can be sorted in advance, doing nested loops will certainly be quicker. Of course, if there is no sort, Nested Loops join can be done, that is, cost will greatly increase.3. It is best to have an index on Inner table that supports retrieval.The nested loop algorithm takes each value of outer table one at a time, looking for all the qualifying rec

started competing on Kaggle?With all my experience, I decided my area of greatest interest lay in analysing complex data. I also quickly realised that my coding skills were from the previous century and thus I opted to learn python. Perhaps the best-of-learn a programming language is to actually does something in it and this led me to Kaggle.Peter ' s top competition finishesDo you have any prior experience or domain knowledge that helped you succeed

business in the period of renewal, such as predicting the overdue and delaying behaviors of the clients, which is only applicable to the personal financing subject. Collection Rating Model: mainly used in related financing business in the stock of customers need to collect the forecast management, only applicable to the individual financing subject. Fraud Rating Model: It is mainly applied to the predictive management of potential fraudulent behaviors of new clients in related financing

explains how to explore and tap into the high-value data behind social networking sites.The tutorial divides the entire mining process into four steps, as follows: Hypothesis: The first step in a data science experiment is to set a goal, answer a question or validate a hypothesis; Acquisition: Acquiring and storing the data required during the validation process; Analysis: Using basic data mining techniques to analyze the data; Summary: The results of the excavation are pre

/hollywood2/Hollywood human Behavior Library http://vision.stanford.edu/Datasets/olympicsports/olympic Sports DataSet These databases are based on action/behavior recognition (in the 1th URL can also find their download address), the article "Video of behavioral Recognition public database Summary" The evaluation of them is more pertinent, as can be see: http://blog.sina.com.cn/s/blog_631a4cc40101138j.html 8, Http://homepages.inf.ed.ac.uk/rbf/BEHAVE /

Slave from EDN: http://edndoc.esri.com/arcobjects/9.2/NET/c45379b5-fbf2-405c-9a36-ea6690f295b2.htm Overview of data conversion and transfer Within the geodatabase and geodatabase user interface (UI) libraries, there are five main interfaces involved with transferring datasets from one workspace to another. See the following topics: IFeatureDataConverter and IFeatureDataConverter2 IGeoDBDataTransfer (also known as copy/paste) IDataset (specifically,

),2) Overall Error of k=99 model:0.15Overall Error of K=1 model:0.15?? In fact, it looks like these models are about as good as the test set. The following are the decision boundaries that are learned from the training set to be applied to the test set. See if we can figure out the prediction of two model errors.?? There are different reasons for the two model errors. It seems that the k=99 model is not very good at capturing the features of the crescent-shaped data (this is under-fitting), whe

Domain-shift. In the final analysis, the object detection is not good at present, or because there are a large number of very small objects exist itself, and small objects detection is difficult because: Small objects because small, the internal scale difference is very large (multiples, because the denominator is very small, one will be very large), the detector needs a strong scale-invariance ability, and CNN on its design itself is not Scale-invariance; Small objects it

perform computational logic on XML data before storing the XML data in a SQL Server database. However, since OPENXML is a server-based technology, it can degrade SQL Server performance if you use it frequently or if you have a large number of documents. However, if you are using Microsoft. NET Framework components, you can use ado.net datasets to circumvent these performance and scalability constraints, ado.net d