Natural Language Generation (NLG) is a branch of AI that converts data into human-readable text. Unlike NLP, which focuses on understanding text, NLG focuses on creating meaningful and coherent language…

Policy Gradient is a reinforcement learning approach where the agent learns a policy directly by optimizing the probability of taking the best actions. Unlike value-based methods like Q-Learning, policy gradient…

A Markov Decision Process (MDP) is a mathematical framework used in reinforcement learning to model decision-making situations where outcomes are partly random and partly controlled by an agent. It provides…

A Deep Q-Network (DQN) is an advanced reinforcement learning model that combines Q-Learning with deep neural networks to handle complex, high-dimensional environments. Unlike standard Q-Learning, which uses a Q-table, DQNs…

Q-Learning is a model-free reinforcement learning algorithm that helps an agent learn the best action to take in a given state to maximize cumulative rewards. It doesn’t require a model…

Reinforcement Learning (RL) is a type of AI where an agent learns by interacting with its environment, receiving rewards for good actions and penalties for bad ones. It’s inspired by…

Generative Adversarial Networks, or GANs, are a type of generative AI where two neural networks compete to create realistic data. Generator: Creates new data samples Discriminator: Evaluates and distinguishes real…

Generative AI refers to AI systems that can create new content, such as text, images, audio, and videos, instead of just analyzing data. These models learn patterns from existing data…

The Transformer model is a deep learning architecture that has revolutionized NLP and AI. Unlike RNNs, it processes sequences in parallel, making it faster and more effective for large datasets.…

Long Short-Term Memory (LSTM) is a type of Recurrent Neural Network (RNN) designed to overcome the vanishing gradient problem in standard RNNs. LSTMs are excellent at capturing long-term dependencies in…