Welcome to Royfactory

Introduction Prophet is a calendar-aware additive model that mixes trend, seasonalities, and holiday effects with robust defaults, making it ideal for fast, business-facing forecasts. It ships for Python and R, is resilient to missing data/outliers, and includes built-in tools for time-series cross-validation. Recent 1.1.x releases rely on a cmdstan backend and add practical improvements like scaling. Core Model Concepts Additive structure (y(t)=g(t)+s(t)+h(t)+\epsilon_t) with linear/logistic trend (g(t)), yearly/weekly/daily seasonalities (s(t)), holiday effects (h(t)), and noise. Works best when strong calendar seasonality exists and you have several seasons of history. ...

Introduction Quantum computing leverages superposition, entanglement, and interference to amplify useful outcomes and suppress others. It is not a universal speed-up for every task; instead, it offers algorithmic advantages for specific structures such as period finding, unstructured search, and quantum simulations. Qubits and Measurement A qubit is a normalized superposition α|0⟩ + β|1⟩. Measurement probabilistically collapses the state to 0 or 1. The Bloch sphere provides an intuitive geometric picture of single-qubit states. ...

Introduction Generative Adversarial Networks (GANs) are one of the most exciting innovations in artificial intelligence. Introduced by Ian Goodfellow in 2014, GANs are capable of generating new, realistic data such as images, audio, and even text. The key idea is that two neural networks — a Generator and a Discriminator — compete with each other, improving through this adversarial process. In this post, we’ll explore how GANs work, their mathematical foundation, practical applications, and limitations in a way that is easy to understand for beginners. ...

Introduction This post walks through installing the NVIDIA driver so that nvidia-smi works on Ubuntu, setting up the CUDA Toolkit, and validating the stack with a tiny kernel and deviceQuery. It targets Ubuntu 22.04/24.04 (20.04 is similar). Prepare the System Verify GPU visibility: 1 lspci | grep -i nvidia Update packages and tools: 1 2 sudo apt update && sudo apt -y upgrade sudo apt -y install build-essential dkms linux-headers-$(uname -r) wget git Check Secure Boot status: 1 mokutil --sb-state Install NVIDIA Driver (includes nvidia-smi) 1 2 3 ubuntu-drivers devices sudo ubuntu-drivers autoinstall sudo reboot After reboot: ...

Introduction Nano Banana is Google DeepMind’s codename for Gemini 2.5 Flash Image, a state-of-the-art model for native image generation and editing. It brings natural-language targeted edits, identity consistency across scenes, multi-image fusion, world-knowledge-guided edits, and SynthID watermarking to keep provenance intact. It’s available in the Gemini app and via API (AI Studio / Vertex AI). Pricing is transparent at about $0.039 per image. :contentReference[oaicite:29]{index=29} What’s New Identity Consistency Keep a person, pet, or product looking like itself across variations—perfect for brand sets or episodic content. :contentReference[oaicite:30]{index=30} ...

AI Project Planning and Real-World Applications (Lecture 20) This is the final lecture of our 20-part series. We’ll conclude by discussing how to plan, design, and execute AI projects in real-world scenarios. You’ll learn about the AI project lifecycle, practical applications in various industries, and how to deploy models into production. Table of Contents {% toc %} 1) AI Project Lifecycle AI projects go beyond just training a model. They require a complete end-to-end strategy: ...

Multimodal AI Basics: Text + Image Understanding with CLIP and BLIP (Lecture 19) In this lecture, we’ll explore Multimodal AI, which combines different modalities like text and images to create more powerful and human-like AI systems. Just as humans can read a sentence while looking at a picture, multimodal AI models learn to connect language and vision. Table of Contents {% toc %} 1) What is Multimodal AI? Modality: A type of input data (e.g., text, image, audio) Multimodal AI: Processes and integrates multiple modalities at once Examples: Image Captioning → Generate a description of an image Text-to-Image Retrieval → Find images based on text queries Text-to-Image Generation → Create images from textual prompts (e.g., DALL·E, Stable Diffusion) 2) Why Is It Important? Human-like intelligence: Humans naturally combine vision, speech, and text Expanded applications: Search engines, recommendation systems, self-driving cars, healthcare Generative AI growth: Beyond text-only, multimodal AI powers new experiences like text-to-image and text-to-video 3) Key Multimodal Models CLIP (Contrastive Language-Image Pretraining) – OpenAI ...

Transformer Applications: Summarization and Translation (Lecture 18) In this lecture, we’ll explore two of the most practical applications of Transformers: text summarization and machine translation. Transformers excel at both tasks by leveraging their self-attention mechanism, which captures long-range dependencies and contextual meaning far better than RNN-based models. Table of Contents {% toc %} 1) Text Summarization Text summarization comes in two main forms: Extractive Summarization Selects key sentences directly from the original text. Example: Picking the 2–3 most important sentences from a news article. ...

GPT Basics: Generative Pretrained Transformer Explained (Lecture 17) In this lecture, we’ll explore GPT (Generative Pretrained Transformer), a Transformer-based model introduced by OpenAI in 2018. While BERT excels at understanding text (encoder-based), GPT specializes in generating text (decoder-based). GPT has since evolved into the foundation of ChatGPT and GPT-4. Table of Contents {% toc %} 1) Why GPT? GPT is designed to predict the next token in a sequence (autoregressive modeling). This makes it excellent at generating coherent, human-like text. ...

BERT Architecture and Pretraining: From MLM to NSP (Lecture 16) In this lecture, we’ll explore BERT (Bidirectional Encoder Representations from Transformers), a groundbreaking model introduced by Google in 2018. BERT significantly advanced NLP by introducing bidirectional context learning and a pretraining + fine-tuning framework, becoming the foundation for many state-of-the-art models. Table of Contents {% toc %} 1) Why BERT? Previous language models read text in only one direction (left-to-right or right-to-left). BERT, however, learns context from both directions simultaneously, making it far better at understanding word meaning in context. ...