Ashish Patel 🇮🇳’s Post

𝗗𝗮𝘆-𝟭𝟲𝟲 Computer Vision Learning 𝗚𝗿𝗮𝗽𝗵 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 in TensorFlow and Keras with Spektral by Daniele Grattarola, Cesare Alippi Follow me for a similar post:  🇮🇳 Ashish Patel Interesting Facts : 🔸 This is a paper in arXiv with over 25 citations. ------------------------------------------------------------------- 𝗔𝗺𝗮𝘇𝗶𝗻𝗴 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 : https://lnkd.in/dNjAg9c code: https://lnkd.in/dReQGtj Dataset: https://lnkd.in/dTwmAbn Official website: https://lnkd.in/dr82pNG ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸 Spektral, an open-source Python library for building graph neural networks with TensorFlow and the Keras application programming interface. Spektral implements a large set of methods for deep learning on graphs, including message-passing and pooling operators, as well as utilities for processing graphs and loading popular benchmark datasets. The purpose of this library is to provide the essential building blocks for creating graph neural networks, focusing on the guiding principles of user-friendliness and quick prototyping on which Keras is based. 🔸 Spektral is, therefore, suitable for absolute beginners and expert deep learning practitioners alike. In this work, we present an overview of Spektral’s features and report the performance of the methods implemented by the library in scenarios of node classification, graph classification, and graph regression. #computervision #innovation #artificialintelligence