AI Knowledge

Artificial Intelligence (AI) is the science of building intelligent machines that can simulate human thinking, learning, and decision-making. By analysing data and learning from experience, AI can understand language, recognize complex patterns, make independent decisions, and automate tasks without step-by-step human intervention.

Algorithms are the heart of AI. They are precise, step-by-step set of instructions or rules that a computer follows to solve a problem or complete a task. Think of it like a recipe, following whose set of rules or instructions a chef can cook the dish he or she wants (solve a problem or accomplish a task).

Parameters are the internal variables that an AI model adjusts during its learning process to map inputs to accurate outputs. Acting as the model's "memory" and knowledge tuning knobs, the quantity (can run into billions or trillions) and quality of parameters determine an AI's capacity to recognize complex patterns and perform task reasoning.

Machine Learning (ML) is a subset of AI and the science of training a computer program or system to learn from data and perform tasks without explicit instructions or programming. Instead of telling them every single step, we let them find patterns and make predictions on their own, getting smarter over time.

Deep Learning is a specialized subset of machine learning inspired by the human brain, using multi-layered artificial neural networks to analyse complex data. By processing inputs through deep computational layers, it automatically extracts intricate patterns to master tasks like speech, vision, and language generation.

A neural network is a machine learning framework designed to process information like a human brain. By filtering data through interconnected layers of digital nodes, it detects subtle patterns, adjusts its internal connections based on trial and error, and powers advanced AI tasks like speech and image recognition.

Supervised Learning is a common way Machine Learning (ML) models learn. It's like teaching an Artificial Intelligence (AI) with a teacher – you show the AI many examples (Data) where you already provide the correct answer or label, and the AI learns to connect the data to the right answer.

Unsupervised learning is a machine learning approach where an algorithm analyses unlabelled datasets to discover hidden patterns, groupings, or structures without human guidance or explicit target outputs. It relies on inherent data characteristics to perform tasks like clustering, dimensionality reduction, and anomaly detection.

A Convolutional Neural Network (CNN) is a special type of Neural Network that is very good at understanding visual information, like images. It works by examining small parts of a picture to find basic patterns and then combining these patterns to recognize larger objects or features in the whole image.

A Recurrent Neural Network (RNN) is a type of Neural Network designed to work with data that comes in a sequence, like words in a sentence or numbers over time. RNNs have a kind of internal "memory" that lets them use information from previous steps in the sequence to help understand the current step.