Concepts, made simple.

Attention is a mechanism that lets a model focus on the most relevant parts of input data when making predictions.

Deep Learning is a type of machine learning that uses many-layered neural networks to automatically learn patterns from vast amounts of data.

Gradient Descent is an optimization algorithm that iteratively adjusts parameters to minimize a loss function by moving in the direction of steepest descent.

A Large Language Model is an AI trained on vast text data to predict and generate human-like text.

Machine Learning is a way for computers to learn from data without being explicitly programmed for every task.

A neural network is a computer system inspired by the brain that learns patterns from data to make decisions or predictions.

A Transformer is a neural network architecture that processes all parts of a sequence simultaneously using self-attention, revolutionizing AI tasks like language translation and text generation.

Embedding is a way to turn words, images, or other data into a list of numbers that captures their meaning and relationships.

A GAN pits two neural networks against each other to generate new, realistic data from scratch.

Overfitting is when a model learns the training data too perfectly, including its noise, and fails to generalize to new data.

Reinforcement Learning is a type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize a reward signal.

Fine-tuning is the process of taking a pre-trained AI model and further training it on a smaller, specific dataset to adapt it for a particular task.

Hallucination is when an AI makes up confident-sounding but false information.

Prompt engineering is the practice of designing and refining input prompts to get the best possible output from an AI language model.

RAG (Retrieval-Augmented Generation) is a technique that lets AI models look up external information before answering, making their responses more accurate and up-to-date.