An easy-to-understand explanation of critical elements of Reinforcement learning — If you are trying to learn a new skill like roller skating, you will probably fall several times before mastering the art of skating. You will be rewarded a successful landing every time you learn to balance. Every time you are not able to balance yourself, you will receive a…

An Intuitive explanation of Supervised, Unsupervised, and Reinforcement learning along with the differences — Machine Learning(ML) is a subset of Artificial Intelligence(AI), defined as a computer's ability to learn from data by using algorithms to imitate intelligent human behavior of decision making and predictions. There are three main groups of algorithms in ML: Supervised learning, Unsupervised learning

Explaining the predictions of Graph Neural Network using GNNExplainer — This article explores Need for explainability for GNNs Challenges explaining GNN predictions Different GNN explanation approaches Intuitive explanation of GNNExplainer Implementation using GNNExplainer to explain Node classification and Graph Classification Explainability of a Deep Learning model provides a human-understandable reasoning of their predictions.

Implement GCN, GraphSAGE, and GAT on PubMed using PyTorch_Geometric — Dataset: The PubMed dataset consists of scientific publications from the PubMed database on types of diabetes classified into one of three classes Diabetes Mellitus Experimental Diabetes Mellitus Type_1 Diabetes Mellitus Type_2 The citation network consists of 44,338 links. Each publication in the dataset is described by a TF/IDF weighted word vector…

Node Classification on Cora Dataset in PyTorch using GCN — Prerequisites: Graph Basics and Application of Graph Graph Representational Learning Graph Neural Networks: A Deep Neural Network for Graphs Dataset: This article uses Cora Dataset, consisting of 2708 scientific publications classified into one of seven different classes. The citation network consists of 5429 links. Objective: Node classification using GCN to…

Explore different Graph Neural Networks (GNN): GCN, GraphSAGE, and GAT — Prerequisites: Graph Basics and Application of Graph Good to read: Graph Representational Learning Graph Neural Network(GNN) is a powerful generic framework for defining deep learning models aiming at addressing graph-related tasks in an end-to-end manner GNNs goal is to generate representations for nodes or any feature information based on the…

Map Nodes or Graphs to a low-dimensional vector space — Prerequisite Graph Basics and Applications of Graph A Graph is a ubiquitous non-linear data structure that describes a complex system. A graph models a set of objects called nodes or vertices and their relationships known as edges or links. …

Know what a Graph is, the types of Graphs, the application of Graphs, and a naive approach to representing Graph data. — How will you visualize your social network, a traffic route, a gene mapping, a molecular bond, a brain graph, or a web graph? Graphs are ubiquitous data structures used to describe a complex system. …

A breakthrough step using a single neural transformer to perform numerous multiple tasks. — Imagine a single neural sequence transformer model with a single set of weights engaged in predicting various tasks like caption images, chat, stacking blocks with a robotic arm, outperforming humans at playing Atari games, navigating in simulated 3D environments, and more. Deep Mind ‘s Gato is a significant step towards…