Category: Programming


Home – Welcome to MLBox’s official documentation — MLBox Documentation

MLBox is a powerful Automated Machine Learning python library. It provides the following features:

Fast reading and distributed data preprocessing/cleaning/formatting.
Highly robust feature selection and leak detection.
Accurate hyper-parameter optimization in high-dimensional space.
State-of-the art predictive models for classification and regression (Deep Learning, Stacking, LightGBM,…).
Prediction with models interpretation.


ryanjay0/miles-deep: Deep Learning Porn Video Classifier/Editor with Caffe

Using a deep convolutional neural network with residual connections, Miles Deep quickly classifies each second of a pornographic video into 6 categories based on sexual act with 95% accuracy. Then it uses that classification to automatically edit the video. It can remove all the scenes not containing sexual contact, or edit out just a specific act.


xgboost-node

XGBoost-Node is the first port of XGBoost to run existing XGBoost model with Node.js.

XGBoost is a library from DMLC. It is designed and optimized for boosted trees. The underlying algorithm of XGBoost is an extension of the classic gbm algorithm. With multi-threads and regularization, XGBoost is able to utilize more computational power and get a more accurate prediction.


SmoothGrad

When a machine learning model makes a prediction, often times we would like to determine which features of the input (pixels, for images) were important for the prediction. If the model makes a misprediction, we might want to know which features contributed to the misclassification. We can visualize the feature importance mask as a grayscale image with the same dimensions as the original image with brightness corresponding to importance of the pixel.


Auto-Keras

Auto-Keras is an open source software library for automated machine learning (AutoML). It is developed by DATA Lab at Texas A&M University and community contributors. The ultimate goal of AutoML is to provide easily accessible deep learning tools to domain experts with limited data science or machine learning background. Auto-Keras provides functions to automatically search for architecture and hyperparameters of deep learning models.


Parenscript

Parenscript is a translator from an extended subset of Common Lisp to JavaScript. Parenscript code can run almost identically on both the browser (as JavaScript) and server (as Common Lisp).

Parenscript code is treated the same way as Common Lisp code, making the full power of Lisp macros available for JavaScript. This provides a web development environment that is unmatched in its ability to reduce code duplication and provide advanced metaprogramming facilities to web developers.


Relaxed JSON

a strict superset of JSON, relaxing strictness of valilla JSON. Valid, vanilla JSON will not be changed by RJSON.transform. But there are few additional features helping writing JSON by hand.

Comments are stripped : // foo and /* bar */ →      . Comments are converted into whitespace, so your formatting is preserved.
Trailing comma is allowed : [1, 2, 3, ] → [1, 2, 3]. Works also in objects { “foo”: “bar”, } → { “foo”: “bar” }.
Identifiers are transformed into strings : { foo: bar } → { “foo”: “bar” }.
Single quoted strings are allowed : ‘say “Hello”‘ → “say “Hello””.
More different characters is supported in identifiers: foo-bar → “foo-bar”.

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