8-Chems

Welcome back to my blog! If you've been following my Symfony series, you built a Task Manager CRUD app with Twig templates. But let's face it—those basic HTML templates can feel a bit dated. To give your app a modern, interactive UI, let's integrate React with Symfony. React will handle the frontend, while Symfony powers the backend via a REST API. This guide is beginner-friendly, assuming basic PHP and JavaScript knowledge. If you're coming from Laravel, I'll draw parallels to make it easier. By the end, you'll have a sleek Task Manager where users can create, view, update, and delete tasks using React's dynamic components, talking to Symfony's API. Let's dive in! Why React with Symfony? Symfony is a robust PHP framework for building APIs and handling backend logic, similar to Laravel. React, a JavaScript library, excels at creating interactive, component-based UIs. Combining them giv...

A Beginner's Guide to Symfony: Build a Task Manager CRUD App Welcome to my blog! If you're new to PHP web development or have dabbled with frameworks like Laravel, this guide is your gateway to Symfony —a powerful, flexible PHP framework for building modern web applications. In this tutorial, we'll create a Task Manager , a simple CRUD (Create, Read, Update, Delete) app, with a focus on crafting a clean front-end using HTML, CSS, and Symfony's Twig templating engine. We'll also dive deep into connecting Symfony to a database using Doctrine, ensuring you understand every step. By the end, you'll have a fully functional app and the confidence to explore Symfony further. This guide is tailored for beginners. I assume you know basic PHP, HTML, CSS, and a bit of SQL. If you're familiar with Laravel, I'll draw comparisons to make the transition smoother. Let's dive in! What is Symfony? ...

Neuro-Symbolic Integration: Enhancing LLMs with Knowledge Graphs Abstract Large Language Models (LLMs) have revolutionized natural language processing, achieving remarkable success in tasks like text generation and question answering. However, their reasoning capabilities are constrained by hallucinations—generating plausible but factually incorrect outputs—and limited parametric memory, which hampers their ability to maintain context over long interactions or perform complex multi-step reasoning. This article synthesizes insights from 2024-2025 surveys on neuro-symbolic artificial intelligence, focusing on integrating LLMs with Knowledge Graphs (KGs) to enhance factual grounding, reasoning, and knowledge management in real-world applications. We explore methodologies for knowledge extraction, representation, reasoning, and dynamic updating, emphasizing bidirectional synergies where LLMs automate KG construction and KGs improve LLM reasoni...

Introducing Hugging Face: Your Gateway to Cutting-Edge Machine Learning Hugging Face has emerged as a powerhouse in the machine learning (ML) community, championing open-source solutions to democratize artificial intelligence. With a mission to advance AI through open science, Hugging Face offers a suite of powerful libraries and tools that simplify the development, training, and deployment of state-of-the-art ML models. Whether you're a researcher, developer, or enthusiast, Hugging Face provides accessible, high-quality resources to bring your ML projects to life. In this post, we’ll explore Hugging Face’s core ML libraries, their functionalities, how you can leverage them for your projects, and an example of using an advanced image-to-image model, along with links for further exploration. What is Hugging Face? Hugging Face is an open-source platform that provides tools, libraries, and a collaborative hub for building, sharing, and deploying...

Understanding and Using the Generalized Pareto Distribution (GPD) The Generalized Pareto Distribution (GPD) is a probability distribution used in Extreme Value Theory to model values that exceed a certain high threshold. It is widely used in finance, insurance, hydrology, and environmental science. 📘 What is the GPD? The GPD models the distribution of excess values over a threshold. That is, if we set a threshold u , the GPD models the distribution of X − u | X > u . 🔣 Probability Density Function (PDF) f(x) = (1 / σ) * (1 + ξ * x / σ)^(-1/ξ - 1) ξ : Shape parameter (controls the heaviness of the tail) σ : Scale parameter (spread) Support: x ≥ 0 if ξ ≥ 0 ; 0 ≤ x ≤ -σ/ξ if ξ < 0 🛠️ Fitting GPD to Synthetic Insurance Claims (Python Example) Let’s simulate a small dataset of insurance claims, set a threshold, and fit a Generalized Pareto Distribution using scipy . 🔢 Step 1: Simulate Data import numpy as np import matplotlib.py...