- 4 Essential Tools any Data Scientist can use to improve their productivity
- A Guided Tour of the Big Data Technologies Zoo
- Build a Machine-Learning App Using Sparkling Water and Apache Spark
- DeepDive - finds correlations in raw text files and other files that aren’t organized
DeepDive is a system to extract value from dark data. Like dark matter, dark data is the great mass of data buried in text, tables, figures, and images, which lacks structure and so is essentially unprocessable by existing software. DeepDive helps bring dark data to light by creating structured data (SQL tables) from unstructured information (text documents) and integrate such data with an existing structured database. DeepDive is used to extract sophisticated relationships between entities and make inferences about facts involving those entities. DeepDive helps one process a wide variety of dark data and put the results into a database. Once in a database, one can use a variety of standard tools that consume structured data, e.g., visualization tools like
- Introduction to several Apache projects
- Microsoft Power BI
Interactive Data Visualization BI Tools
- Rexer Analytics Survey Results
TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.
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- Torch | Scientific computing for LuaJIT
Torch is a scientific computing framework with wide support for machine learning algorithms. It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.
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