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Official code repo for the O'Reilly Book - "Hands-On Large Language Models"
here you can find the material used for our Tutorials
Latex Template for Thesis in University of Isfahan (UI)
Code Repository for Python Object-Oriented Programming - 4th edition, Published by Packt
A graph-transformer for whole slide image classification
AI app to detect breast cancer in histopathological database
📺 Discover the latest machine learning / AI courses on YouTube.
A collection of code snippets from the publication Daily Dose of Data Science on Substack: http://www.dailydoseofds.com/
A collection of design patterns/idioms in Python
Python version of design patterns for humans: https://github.com/kamranahmedse/design-patterns-for-humans
The aim of this repo is making an updated list of all companies around the world that offer visa sponsorship perk.
This repository is a curated collection of links to various courses and resources about Artificial Intelligence (AI)
All Algorithms implemented in Python
Code for Photo-Sketching: Inferring Contour Drawings from Images 🐶
Tensorflow 2 Tutorials (use tensorflow and keras in a better way!)
Detecting Emotions in children drawings using a Convolutional Neural Network
scGNN (single cell graph neural networks) for single cell clustering and imputation using graph neural networks
آموزش دیزاین پترن به زبون آدمیزاد - Teaching design patterns in Persian
دوره جامع یادگیری عمیق با Tensorflow و Keras
Returns latest research results by crawling arxiv papers and summarizing abstracts. Helps you stay afloat with so many new papers everyday.
GraphMAE: Self-Supervised Masked Graph Autoencoders in KDD'22
深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系[email protected] 版权所有，违权必究 Tan 2018.06
Corresponding code of 'Quiros A.C.+, Coudray N.+, Yeaton A., Yang X., Chiriboga L., Karimkhan A., Narula N., Pass H., Moreira A.L., Le Quesne J.*, Tsirigos A.*, and Yuan K.* Mapping the landscape o…
A standardized Python API with necessary preprocessing, machine learning and explainability tools to facilitate graph-analytics in computational pathology.