Introduction In today’s AI-driven world, data is often called the new oil—and for good reason. High-quality, diverse datasets are the backbone of machine learning, powering everything from...
The Natural Language Processing (NLP) Blog
Topic: Optimisation
Hallucinations In LLMs Made Simple: Causes, Detection, And Mitigation Strategies
Introduction Large language models (LLMs) have rapidly become a core component of modern NLP applications, powering chatbots, search assistants, summarization tools, and decision-support systems....
LMOps Made Simple With Extensive Guide: Including Tools List
Introduction: Why LMOps Exist Large Language Models have moved faster than almost any technology in recent memory. In a short time, teams have gone from experimenting with prompts in a browser to...
Stochastic Modelling Made Simple and Step-by-step Tutorial
Introduction Uncertainty is everywhere. Whether we're forecasting tomorrow's weather, predicting customer demand, estimating equipment failure, or modelling the spread of a disease, the truth is the...
Entropy In Information Theory Made Simple With Examples & Python Tutorial
Introduction In a world overflowing with data, one question quietly sits at the heart of every message we send, every prediction we make, and every system we build: how much uncertainty is there?...
Genetic Algorithms Made Simple With Examples And Parameter Tuning
Introduction Imagine nature as the world's most powerful problem solver — endlessly experimenting, selecting, and refining through millions of years of evolution. What if we could capture that same...
Taylor Series Made Simple With Examples And Applications
Introduction: The Magic of Approximation Have you ever wondered how your calculator instantly knows the value of sin(30°), e², or ln(5)? Behind that apparent magic lies a mathematical masterpiece...
![Hill Climbing Algorithm In AI Made Simple [Examples & How To Tutorial]](https://i0.wp.com/spotintelligence.com/wp-content/uploads/2025/10/graph.png?fit=1276%2C1266&ssl=1)
Hill Climbing Algorithm In AI Made Simple [Examples & How To Tutorial]
Introduction In the world of Artificial Intelligence (AI), many problems—such as route optimisation, game strategy, or model tuning—reduce to a straightforward question: How can we find the best...
Simulated Annealing Made Simple & How To Tutorial In Python
Introduction: The Search for the Best Solution Imagine you’re trying to find the fastest route through a city with hundreds of streets, or the optimal design for a complex circuit. The number of...
Particle Swarm Optimization (PSO) Explained With How To Python Tutorial
Introduction Optimization lies at the heart of nearly every scientific and engineering challenge — from tuning the hyperparameters of a machine learning model to designing aerodynamic vehicles or...
![Machine Learning For Documents [How It Works & 15 Popular Tools]](https://i0.wp.com/spotintelligence.com/wp-content/uploads/2025/10/ml-pipeline-for-documents.jpg?fit=1200%2C675&ssl=1)
Machine Learning For Documents [How It Works & 15 Popular Tools]
Introduction Every organisation today is flooded with documents — contracts, invoices, reports, customer feedback, medical records, research papers, and more. These documents hold critical...
Embedding Models Explained, How To Use Them & 10 Tools/Frameworks
What Are Embedding Models? At their core, embedding models are tools that convert complex data—such as words, sentences, images, or even audio—into numerical representations. More specifically, they...
Vector Embeddings Made Simple & How To Tutorial In Python
What Are Vector Embeddings? Imagine trying to explain to a computer that the words "cat" and "dog" are more similar to each other than to "car". Computers don't inherently understand language,...
![Monte Carlo Tree Search Explained & How To Implement [With Code]](https://i0.wp.com/spotintelligence.com/wp-content/uploads/2025/09/four-steps-monte-carlo-tree-search.jpg?fit=1200%2C675&ssl=1)
Monte Carlo Tree Search Explained & How To Implement [With Code]
What is Monte Carlo Tree Search? Monte Carlo Tree Search (MCTS) is a decision-making algorithm that helps an agent figure out the best action when the possible outcomes are too vast to calculate...
Dynamic Programming Explained & How To Tutorial In Python
What is Dynamic Programming? Dynamic Programming (DP) is a powerful algorithmic technique used to solve complex problems by breaking them down into simpler, overlapping subproblems. Instead of...
Temporal Difference Learning Made Simple With Example & Alternatives
What is Temporal Difference Learning? Temporal Difference (TD) Learning is a core idea in reinforcement learning (RL), where an agent learns to make better decisions by interacting with its...
Understanding Interdependent Variables: The Hidden Web Of Cause And Effect
Have you ever wondered why raising interest rates slows down inflation, or why cutting down forests affects rainfall patterns? These everyday phenomena are driven by a web of connections where one...
Deep Deterministic Policy Gradient Made Simple & How To Tutorial In Python
Introduction Reinforcement Learning (RL) has seen explosive growth in recent years, powering breakthroughs in robotics, game playing, and autonomous control. While early successes focused on...
