Building neural networks from scratch in Python with NumPy is one of the most effective ways to internalize deep learning fundamentals. By coding forward and backward propagation yourself, you see how ...

Overview Structured Python learning path that moves from fundamentals (syntax, loops, functions) to real data science tools ...

Analogue engineering still relies heavily on manual intervention, but that is changing with the growing use of AI/ML.

The first component is the Market Data Gateway (or API Wrapper). This layer creates a persistent connection to the exchange's servers, translating raw 'JSON' or 'FIX' messages into clean Python data ...

Abstract: Graph neural networks (GNNs), as a cutting-edge technology in deep learning, perform particularly well in various tasks that process graph structure data. However, their foundation on ...

Learn how to create dynamic, animated graphs in GlowScript using VPython with ease! 📊 This step-by-step tutorial guides you through visualizing data, animating simulations, and mastering interactive ...

This repository contains a refactoring of the code used in the paper "Learning Latent Graph Structures and Their Uncertainty" (ICML 2025). The code is designed to be modular and easy to use, allowing ...

This repository contains my complete solutions to the legendary Karan's Mega Project List — a curated collection of programming challenges designed to improve coding skills across multiple domains.

Getting started with LeetCode can feel like a lot, especially if you’re just beginning your coding journey. So many problems, so many concepts – it’s easy to get lost. But don’t sweat it. This guide ...

Abstract: This paper presents a novel approach to graph learning, GL-AR, which leverages estimated autoregressive coefficients to recover undirected graph structures from time-series graph signals ...