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机器学习
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- 维基百科
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.
Collection of models for Core ML
A list of popular deep learning models related to classification, segmentation and detection problems
Linear Prediction Model with Automated Feature Engineering and Selection Capabilities
A package that makes it trivial to create and evaluate machine learning pipeline architectures.
Localize and identify multiple objects in a single image.
The World's First Realtime onDevice AI Project to maintian Modestey and Religious values. Everyday AI companion designed for Muslim community to empower them maintain modesty, decency in the digital realm by blurring and covering Non-Mahram and Haram objects (NSFW), directly on screen on anywhere(offline,online,browser,videoplayer) in anytime.
State-of-the art Automated Machine Learning python library for Tabular Data
Useful resources for creating projects with Teachable Machine models + curated list of already built Awesome Apps!
Google Cloud Platform (GCP) Guide. Learn all about Google Cloud Tools, Services, and Certifications.
Identify sounds in short audio clips
A CoreML model which classifies images of food
🤖 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
Detect humans in an image and estimate the pose for each person
An efficient open-source AutoML system for automating machine learning lifecycle, including feature engineering, neural architecture search, and hyper-parameter tuning.