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.
💫 Models for the spaCy Natural Language Processing (NLP) library
Upscale an image by a factor of 4, while generating photo-realistic details.
Machine learning Guide. Learn all about Machine Learning Tools, Libraries, Frameworks, Large Language Models (LLMs), and Training Models.
Linear Prediction Model with Automated Feature Engineering and Selection Capabilities
Localize and identify multiple objects in a single image.
State-of-the art Automated Machine Learning python library for Tabular Data
Identify sounds in short audio clips
🤖 An automated machine learning framework for audio, text, image, video, or .CSV files (50+ featurizers and 15+ model trainers). Python 3.6 required.
Deep active inference agents using Monte-Carlo methods
Predict hourly weather features given historical data for a specific location
Generate embedding vectors from audio files
An efficient open-source AutoML system for automating machine learning lifecycle, including feature engineering, neural architecture search, and hyper-parameter tuning.
Detect the sentiment captured in short pieces of text
Detect 6 types of toxicity in user comments.
Generate a new image that mixes the content of a source image with the style of another image.
Unofficial implementation of SpecTNT in pytorch
Image classifier for physical places/locations, based on the Places365-CNN Model
Template codes and examples for Python machine learning concepts
Implementation of the method described in the paper "Quasi-unsupervised color constancy" - CVPR 2019
Estimated annual CO2 emissions from diesel generators at mobile or cell towers