Augmentor is an image augmentation library in Python for machine learning. It aims to be a platform and framework independent library, which is more convenient, allows for finer grained control over augmentation, and implements as many augmentation procedures as possible. It employs a stochastic appro...
EpiLearn is a Python machine learning toolkit for epidemic data modeling and analysis. We provide numerous features including: Implementation of Epidemic Models Simulation of Epidemic Spreading Visualization of Epidemic Data Unified Pipeline for Epidemic Tasks For more machine models in epidemic modeling, ...
Python modules ModuleVersionDescription azureml-model-management-sdk1.0.1Classes and functions to authenticate, deploy, manage, and consume analytic web services in Python. microsoftml9.4A collection of Python functions used for machine learning operations, including training and transformations, scorin...
etc. Here we will use a predefined color map (optioncmap) calledjet. As an example, we plot the house prices in different locations and let the radius of each circle represents the district’s population (options), and the color represents the price (optionc). ...
Recent advances in data assimilation at the European Centre for Medium-Range Weather Forecasts (ECMWF) indicate that it is possible to estimate and correct for a large fraction of systematic model error in the stratosphere. The question we address here is whether machine learning techniques can be...
New advancements in biological image processing, such as object segmentation, tracking1 and machine-learning frameworks, have enabled researchers to extract more information and ask additional questions of their image data. Increasingly, these innovations are written in the Python programming language, ...
Python Pandasis an open-source toolkit which provides data scientists and analysts with data manipulation and analysis capabilities using the Python programming language. The Pandas library is very popular in the preprocessing phase of machine learning and deep learning. But now you can do more with...
Developers approached the integration of machine learning capabilities into existing applications in a number of ways. Taking inference as an example, current options vary from using stock API to having a Python or C++ based application wrapped with an API for remote calls. Stoc...
oneDAL optimizes algorithms from popular machine learning Python* libraries such as XGBoost and Intel® Extension for Scikit-learn*, which are part of the end-to-end suite of Intel® AI and machine learningdevelopment tools and resources. ...
Thinc: Practical Machine Learning for NLP in Python Thincis the machine learning library poweringspaCy. It features a battle-tested linear model designed for large sparse learning problems, and a flexible neural network model under development forspaCy v2.0. ...