| Natural Language Generation (NLG) |
NLG is a subfield of artificial intelligence that focuses on generating natural language text from structured data. It automates the process of turning data into written narratives. |
Chatbots, Report generation, Content creation |
Wikipedia |
| Natural Language Processing (NLP) |
NLP involves the interaction between computers and humans through natural language. It enables machines to understand, interpret, and generate human language. |
Text analysis, Speech recognition, Machine translation |
Wikipedia |
| Convolutional Neural Networks (CNN) |
CNNs are a class of deep neural networks, most commonly applied to analyzing visual imagery. They are designed to automatically and adaptively learn spatial hierarchies of features. |
Image classification, Object detection, Facial recognition |
Wikipedia |
| Computer Vision |
Computer Vision is a field of AI that enables computers to interpret and make decisions based on visual data. It involves acquiring, processing, analyzing, and understanding images and videos. |
Autonomous vehicles, Medical imaging, Surveillance |
Wikipedia |
| Generative AI |
Generative AI refers to algorithms that create new content, such as text, images, or music, by learning patterns from existing data. It includes technologies like GANs and VAEs. |
Art creation, Data augmentation, Music composition |
Wikipedia (GANs) |
| Reinforcement Learning |
Reinforcement Learning is a type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize cumulative reward. It is inspired by behavioral psychology. |
Game playing, Robotics, Autonomous control systems |
Wikipedia |
| Transfer Learning |
Transfer Learning involves leveraging knowledge gained from one task to improve learning performance on a related but different task. It is used to apply pre-trained models to new tasks with limited data. |
Fine-tuning pre-trained models, Domain adaptation |
Wikipedia |
| Supervised Learning |
Supervised Learning is a type of machine learning where the model is trained on labeled data. It learns to map input data to the correct output labels. |
Classification, Regression, Spam detection |
Wikipedia |
| Unsupervised Learning |
Unsupervised Learning is a type of machine learning where the model is trained on unlabeled data. It aims to find hidden patterns or intrinsic structures in the input data. |
Clustering, Anomaly detection, Association rule learning |
Wikipedia |
| Semi-Supervised Learning |
Semi-Supervised Learning is a type of machine learning that uses both labeled and unlabeled data for training. It falls between supervised and unsupervised learning. |
Speech analysis, Image classification |
Wikipedia |
| Recurrent Neural Networks (RNN) |
RNNs are a class of neural networks where connections between nodes form a directed graph along a temporal sequence. They are used for sequential data processing. |
Language modeling, Time series prediction, Text generation |
Wikipedia |