Machine learning is the practice of teaching a computer to learn. The concept uses pattern recognition, as well as other forms of predictive algorithms, to make judgments on incoming data. This field is closely related to artificial intelligence and computational statistics.
Collection of models for Core ML
Train and deploy machine learning models for mobile apps with Fritz AI.
A list of popular deep learning models related to classification, segmentation and detection problems
A package that makes it trivial to create and evaluate machine learning pipeline architectures.
Upscale an image by a factor of 4, while generating photo-realistic details.
Linear Prediction Model with Automated Feature Engineering and Selection Capabilities
Launch machine learning models into production using flask, docker etc.
A CoreML model which classifies images of food
Identify sounds in short audio clips
Localize and identify multiple objects in a single image.
State-of-the art Automated Machine Learning python library for Tabular Data
Keras implementation of sketch inversion using deep convolution neural networks (synthesising photo-realistic images from pencil sketches)
MLModelScope is an open source, extensible, and customizable platform to facilitate evaluation and measurement of ML models within AI pipelines.
Generate a new image that mixes the content of a source image with the style of another image.
Detect humans in an image and estimate the pose for each person
Identify objects in an image, additionally assigning each pixel of the image to a particular object
Detect the sentiment captured in short pieces of text
Image classifier for physical places/locations, based on the Places365-CNN Model
Identify objects in images using a third-generation deep residual network.
Generate embedding vectors from audio files
Template codes and examples for Python machine learning concepts
Generate English-language text similar to the news articles in the One Billion Words data set.
Categorize sports videos according to which sport the video depicts.
Similar to https://github.com/IBM/MAX-Object-Detector/blob/master/demo.ipynb, which illustrates how to invoke the prediction endpoint and parse the results. (~ "notebook-based" swagger spec for a data scientist audience)
Detect whether a mitosis exists in an image of breast cancer tumor cells
Implementation of the method described in the paper "Quasi-unsupervised color constancy" - CVPR 2019
HOURLYPressureTendency is actually a categorical variable. From the NOAA LCD documentation: