Welcome to Royfactory

AI Development Environment Setup: Anaconda, Jupyter, and GPU Acceleration (Lecture 4) In this lecture, we’ll set up a stable AI development environment for Machine Learning and Deep Learning projects. You’ll learn how to install Anaconda, run Jupyter Notebook, and configure GPU acceleration with CUDA and cuDNN. Table of Contents {% toc %} 1) Why Environment Setup Matters A well-configured environment prevents common issues such as: Library version conflicts Slow training due to CPU-only execution Non-reproducible results across team members Goals: ...

Kubernetes Pod Resource Management: Requests, Limits, and QoS Question: “How do I control CPU and memory usage for Pods in Kubernetes?” If resource limits are not set, a single Pod can consume excessive CPU or memory, causing instability across the cluster. Kubernetes provides Requests and Limits to manage resources effectively.

Q-Learning and CartPole: Your First Reinforcement Learning Agent If you’ve dipped your toes into reinforcement learning, chances are you’ve encountered Q-Learning — a classic, foundational algorithm that’s simple to understand yet powerful enough to teach you how AI agents can learn from rewards. In this post, you’ll learn: What Q-Learning is and how it works Why it’s great for beginners How to apply it to a real environment: CartPole from OpenAI Gym A complete, working Python example Let’s get started! ...

Mastering CartPole with DQN: Deep Reinforcement Learning for Beginners If you’ve played with reinforcement learning (RL) before, you’ve probably seen the classic CartPole balancing problem. And if you’ve tried solving it with traditional Q-learning, you might have run into some limitations. That’s where DQN — Deep Q-Network — comes in. In this guide, we’ll explain what DQN is, why it was a breakthrough in RL, and how to implement it step-by-step to solve the CartPole-v1 environment using OpenAI Gym and PyTorch. Whether you’re new to RL or ready to level up from Q-tables, this tutorial is for you. ...

Reinforcement Learning for Beginners: Build Your First AI Agent with OpenAI Gym Reinforcement Learning (RL) might sound like an advanced topic reserved for researchers and PhDs — but the truth is, you can start today, even as a beginner. This guide will walk you through RL in the simplest terms, using the powerful and easy-to-use OpenAI Gym framework. With just a bit of Python knowledge, you’ll build your first AI agent that interacts with an environment, makes decisions, and learns from rewards — just like a human learning to ride a bike. ...

LangGraph: Build Multi-Turn AI Workflows with Graph Logic As AI agents become more complex and conversational, traditional linear workflows just don’t cut it anymore. Enter LangGraph — a powerful new framework that lets developers define graph-based, stateful AI workflows that support branching, looping, and conditional logic. Built on top of LangChain, LangGraph brings structure, clarity, and flexibility to how you build AI-powered applications. Table of Contents 1. What is LangGraph? LangGraph is an open-source framework created by the LangChain team. While LangChain focuses on chaining components in a sequence (like “A → B → C”), LangGraph lets you build agent systems as graphs where each node is a function or task, and edges define how data flows between them — even conditionally. ...

In this guide, we’ll explore Longhorn, a lightweight, reliable, and open-source distributed block storage system for Kubernetes. You’ll learn what Longhorn is, how it works, how to install it using Helm, and how to monitor it with Grafana and Prometheus. Table of Contents 1. What is Longhorn? Longhorn turns the local disks of your Kubernetes cluster nodes into persistent volumes — making stateful apps like databases possible in Kubernetes. Think of it this way: ...

What is H2O AutoML? Train ML Models Without Coding H2O AutoML is an open-source tool that automates the machine learning process—from data preprocessing to model training, tuning, and selection. It’s built by H2O.ai and is designed for both beginners and experts who want to save time and get powerful models without diving deep into code. Whether you’re analyzing customer churn or predicting sales, H2O AutoML can help you build production-ready models in minutes. ...

What is AutoML? Learn Automated Machine Learning with Python AutoML (Automated Machine Learning) refers to technologies that automate the entire machine learning pipeline, including data preprocessing, model selection, hyperparameter tuning, and evaluation. With AutoML, even beginners can build accurate ML models without deep technical expertise. Table of Contents Why AutoML? Imagine you’re running a coffee shop and want to predict which customers are likely to order an Americano. Building a machine learning model from scratch would require: ...

What is Lasso Regression? Lasso Regression is an extension of linear regression designed to reduce overfitting and improve model simplicity. It’s especially helpful when dealing with high-dimensional data by automatically eliminating irrelevant features, making the model more interpretable and efficient. Table of Contents Concept of Lasso Regression Lasso stands for Least Absolute Shrinkage and Selection Operator. It is a linear regression model that includes an L1 regularization term to penalize large coefficients. ...