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…

Recurrent Neural Networks, or RNNs, are deep learning models designed to handle sequential data, where the order of data points matters. Unlike traditional neural networks, RNNs remember previous inputs in…

A Convolutional Neural Network (CNN) is a type of deep learning model specifically designed for processing visual data like images and videos. CNNs automatically detect patterns, features, and spatial hierarchies…

Computer Vision is a branch of AI that allows machines to see, interpret, and understand visual data from the world, such as images and videos. It enables applications like object…

Speech recognition is an AI technology that allows machines to convert spoken language into text. It enables computers to understand human speech and interact naturally with users. This technology powers…