MLDB is the Machine Learning Database. It’s the best way to get machine learning or AI into your applications or personal projects. Head on over to MLDB.ai to try it right now or see Running MLDB for installation details.
We’re happy to announce the immediate availability of MLDB version 2016.10.05.0.
This release contains 141 new commits and modified 903 files. On top of many bug fixes and performance improvements, here are some of the highlights of this release:
A big new feature is support for importing and exporting data to and from MongoDB, a popular NoSQL database. Although MongoDB can be very useful for certain use cases, it doesn’t have any machine learning capabilities. We want to make it as easy as possible for our users to get their data in MLDB. So we have added the following new MLDB entities that make it easy to interface with MongoDB:
We updated the TensorFlow version shipped with MLDB to version 0.10.0. The new version includes many bug fixes and performance improvements. We’re now also shipping MLDB with different TensorFlow kernels, each optimized for different instruction sets. So for instance, the kernel with AVX2 instructions will be used if it the processor on which MLDB is run supports it.
If you’re interested in deep learning, make sure to checkout the Tensorflow Image Recognition Tutorial and the Transfer Learning with Tensorflow demo to see how easy it is to run trained models with MLDB.
An example of what this benefits is the
jseval can be used.
SELECT statement of the
import.text procedure has been improved to support the
CASE keyword. The adds extra flexibility to process data as it is being imported.
We also fixed a bug when using the
NAMED clause with the
import.json procedure that could cause undesired behaviour.
We have improved the user experience around configuring supervised algorithms in two ways.
First, we have clarified the documentation by creating a new Classifier configuration section that contains the information related to the configuration of supervised models. When using one of the two procedures that can be used to train models, the classifier.train and classifier.experiment, all the information you need to configure your algorithm now lives in one place.
Second, we have made the training more robust to configuration errors by having better validation of elements meant to control hyper-parameters. Incorrect parameters will now trigger errors.
We added two new vector space functions:
First, the new
reshape(val, shape) function takes an n-dimensional embedding and reinterprets it as an N-dimensional embedding of the provided shape containing all of the elements. This allows, for example, a 1-dimensional vector to be reinterpreted as a 2-dimensional array. The shape argument is an embedding containing the size of each dimension.
Second, the new
shape(val) takes an n-dimensional embedding and returns the size of each dimension as an array.
COLUMN EXPRexpression now supports the
STRUCTUREDkeyword. By default,
COLUMN EXPRreturns a flattened representation. Adding the
STRUCTUREDkeyword will return the structured representation.
tsne.trainprocedure now has a
f. The name has been renamed to
HTTP 1.1 100 CONTINUErequest header.
fetcheris now available as a built-in function
levenshtein_distance()function where it did not work properly with UTF-8 characters.
serveStaticFolder()function where the path to serve would not be considered relative to the plugin’s installation directory.
string_agg(expr, separator [, sortField])function, that allows to sort the returned by the