enow.com Web Search

Search results

1. Results from the WOW.Com Content Network

2. Data entry - Wikipedia

   en.wikipedia.org/wiki/Data_entry

   Data entry is the process of digitizing data by entering it into a computer system for organization and management purposes. It is a person-based process [ 1 ] and is "one of the important basic" [ 2 ] tasks needed when no machine-readable version of the information is readily available for planned computer-based analysis or processing.

3. Remote data entry - Wikipedia

   en.wikipedia.org/wiki/Remote_Data_Entry

   A remote data entry (RDE) system is a computerized system designed for the collection of data in electronic format. The term is most commonly applied to a class of software used in the life sciences industry for collecting patient data from participants in clinical research studies—research of new drugs and/or medical devices.

4. Autoencoder - Wikipedia

   en.wikipedia.org/wiki/Autoencoder

   An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning).An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation.

5. IEEE 1394 - Wikipedia

   en.wikipedia.org/wiki/IEEE_1394

   IEEE 1394 is an interface standard for a serial bus for high-speed communications and isochronous real-time data transfer. It was developed in the late 1980s and early 1990s by Apple in cooperation with a number of companies, primarily Sony and Panasonic.

6. Rotary encoder - Wikipedia

   en.wikipedia.org/wiki/Rotary_encoder

   A rotary encoder, also called a shaft encoder, is an electro-mechanical device that converts the angular position or motion of a shaft or axle to analog or digital output signals. [1] There are two main types of rotary encoder: absolute and incremental. The output of an absolute encoder indicates the current shaft position, making it an angle ...

7. Variational autoencoder - Wikipedia

   en.wikipedia.org/wiki/Variational_autoencoder

   These q-distributions are normally parameterized for each individual data point in a separate optimization process. However, variational autoencoders use a neural network as an amortized approach to jointly optimize across data points. In that way, the same parameters are reused for multiple data points, which can result in massive memory savings.