Search Results for "dl"

Super comprehensive deep learning notes is a massive and well-structured collection of deep learning notebooks that serve as a comprehensive study resource for anyone wanting to learn or reinforce concepts in computer vision, natural language processing, deep learning architectures, and even large-model agents. The repository contains hundreds of Jupyter notebooks that are richly annotated and organized by topic, progressing from basic Python and PyTorch fundamentals to advanced neural...

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures! Optimization courses which form the foundation for ML, DL, RL. Computer Vision courses which are DL & ML heavy. Speech recognition courses which are DL heavy. Structured Courses on Geometric, Graph Neural Networks. Section on Autonomous Vehicles. Section on Computer Graphics with ML/DL focus.

MOVED TO https://github.com/acoli-repo/olia. The Ontologies of Linguistic Annotations (OLiA) provide an OWL/DL taxonomy of data categories as a reference for linguistic annotation (OLiA Reference Model), plus OWL/DL models for a large number of annotation schemes (OLiA Annotation Models) and their relationship to reference data categories (OLiA Linking Models). The OLiA Reference Model itself is linked to community-maintained repositories such as GOLD (http://linguistics-ontology.org/) and ISOcat (http://www.isocat.org) The OLiA ontologies were originally developed as part of an infrastructure for the sustainable maintenance of linguistic resources (http://www.sfb441.uni-tuebingen.de/c2/index-engl.html), their fields of application include the formalization of annotation schemes, concept-based querying over heterogeneously annotated corpora, and the development of interoperable NLP pipelines.

...This repository contains an implementation of the following research papers on NLP, CV, ML, and deep learning. The required dependencies are mentioned in requirement.txt. I will also use dl-text modules for preparing the datasets. If you haven't use it, please do have a quick look at it. CV, transfer learning, representation learning.