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The following is a list of machine learning, math, statistics, data visualization and deep learning repositories I have found surfing Github over the past 4 years. AlwaysRemember - Zipfian capstone project - Dan Morris.

Its primary focus is on being generic, clean, and powerful without bitcoin value graph 2014 impala much efficiency. Bitcoin value graph 2014 impala is the merger of the ScalaNLP and Scalala projects, because one of the original maintainers is unable to continue development. The Scalala parts are largely rewritten. Please improve and discuss! Conjecture - Scalable Machine Learning in Scalding. Train Convolutional Neural Networks or ordinary ones in your browser. CoverTree - Python implementation of cover trees, near-drop-in replacement for scipy.

Data-Analysis-and-Machine-Learning-Projects - Repository bitcoin value graph 2014 impala teaching materials, code, and data for my data analysis and machine learning projects. DeepLearningTutorials - Tutorials from deeplearning.

Pre-trained deep neural networks. Your agents are standing by! Other repos in the IPython organization contain things like the website, documentation builds, etc. Content moved to https: Uses Extreme Learning Machines and hyperopt. Kaggle-Competitions - All Kaggle competitions. This book takes a minimally mathematical approach, focusing on building intuition and experience, not formal proofs.

Includes Kalman filters, Extended Kalman filters, unscented filters, and more. Includes exercises with solutions. Kayak - Kayak is a library for automatic differentiation with applications to deep neural networks. LearnDataScience - Open Content for self-directed learning in data bitcoin value graph 2014 impala.

It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in. MechanicalSoup - A Python library for automating interaction with websites. Metronome - Suite of parallel iterative algorithms built on top of Iterative Reduce.

Bringing the python data stack to the shell prompt. Bayesian Stochastic Modelling in Python. Statistical Data Analysis in Python. PythonicPerambulations - A port of jakevdp. Regions with Convolutional Neural Network Features. This is outdated, check out scipy-lecture-notes. SimpleAintEasy - A compendium of the pitfalls and problems that arise when using standard statistical methods. SparseConvNet - Spatially-sparse convolutional networks. Allows processing of sparse 2, 3 and 4 dimensional data.

Advances in Neural Information Processing Systems, Integrates with Apache Storm. TextBlob - Simple, Pythonic, text processing—Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more. Theano-Lights - Deep learning research framework based on Theano. Theano-Tutorials - Bare bones introduction to machine learning from linear regression to convolutional neural networks using Theano. ThinkBayes - Code repository for Think Bayes. Variational-Autoencoder - Implementation of a variational Auto-encoder.

Back To Blog Posts The following is a list of machine learning, math, statistics, data visualization and deep learning repositories I have found surfing Github over the past 4 years. Pre-trained deep neural networks gender-data-pkg - A data package for R containing historical data sets about gender gensim - Topic Modelling for Humans getting-started-with-haskell - notes on where to find Haskell tutorials and tips to complete them ggplot-tutorial - Ghost - Just a blogging platform githut - Visualization of data from bitcoin value graph 2014 impala archive.

Cats competition kaggle-galaxies - Winning solution for the Galaxy Challenge on Kaggle http: Bringing the python data stack to the shell prompt panns - Python Bitcoin value graph 2014 impala Nearest Neighbor Search in very high dimensional space with optimized indexing. ThinkStats2 - Text and supporting code for Think Stats, 2nd Edition thumbor - thumbor is an open-source photo thumbnail service by globo.

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Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data summarization, query and analysis. To accelerate queries, it provides indexes, including bitmap indexes.

HiveQL offers extensions not in SQL, including multitable inserts and create table as select , but only offers basic support for indexes. HiveQL lacked support for transactions and materialized views , and only limited subquery support. Internally, a compiler translates HiveQL statements into a directed acyclic graph of MapReduce , Tez, or Spark jobs, which are submitted to Hadoop for execution.

The word count program counts the number of times each word occurs in the input. The word count can be written in HiveQL as: Checks if table docs exists and drops it if it does. This query serves to split the input words into different rows of a temporary table aliased as temp. This results in the count column holding the number of occurrences for each word of the word column.

The storage and querying operations of Hive closely resemble those of traditional databases. While Hive is a SQL dialect, there are a lot of differences in structure and working of Hive in comparison to relational databases. The differences are mainly because Hive is built on top of the Hadoop ecosystem, and has to comply with the restrictions of Hadoop and MapReduce. A schema is applied to a table in traditional databases. In such traditional databases, the table typically enforces the schema when the data is loaded into the table.

This enables the database to make sure that the data entered follows the representation of the table as specified by the table definition.

This design is called schema on write. In comparison, Hive does not verify the data against the table schema on write.

Instead, it subsequently does run time checks when the data is read. This model is called schema on read. Checking data against table schema during the load time adds extra overhead, which is why traditional databases take a longer time to load data. Quality checks are performed against the data at the load time to ensure that the data is not corrupt.

Early detection of corrupt data ensures early exception handling. Hive, on the other hand, can load data dynamically without any schema check, ensuring a fast initial load, but with the drawback of comparatively slower performance at query time.

Hive does have an advantage when the schema is not available at the load time, but is instead generated later dynamically. Transactions are key operations in traditional databases. Atomicity , Consistency , Isolation , and Durability. Transactions in Hive were introduced in Hive 0. This is because Hadoop does not support row level updates over specific partitions. These partitioned data are immutable and a new table with updated values has to be created.

Hadoop began using Kerberos authorization support to provide security. Kerberos allows for mutual authentication between client and server. The previous versions of Hadoop had several issues such as users being able to spoof their username by setting the hadoop. TaskTracker jobs are run by the user who launched it and the username can no longer be spoofed by setting the hadoop. The Hadoop distributed file system authorization model uses three entities: The default permissions for newly created files can be set by changing the umask value for the Hive configuration variable hive.

From Wikipedia, the free encyclopedia. Apache Hive Developer s Contributors Stable release 2. This section is in a list format that may be better presented using prose.

You can help by converting this section to prose, if appropriate. Editing help is available. Retrieved April 24, Archived from the original on 2 February Retrieved 2 February Spark, Parquet and Avro". Analytics on Blockchain data with SQL".

A Warehousing Solution over a Map-reduce Framework". Journal of Cloud Computing. Retrieved from " https: Pages using deprecated image syntax Articles needing cleanup from October All pages needing cleanup Articles with sections that need to be turned into prose from October Views Read Edit View history.

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