These models power a wide range of AI applications, from visual recognition and content generation to complex decision-making, illustrating the versatility and depth of modern AI technologies.
GPT (Generative Pre-trained Transformer)
Widely used for text generation, chatbots, summarization, and content creation across industries.
BERT (Bidirectional Encoder Representations from Transformers)
Popular for natural language understanding, sentiment analysis, and question answering, commonly used in search engines and NLP tasks.
DALL-E
Well-known for generating high-quality images from text, used in creative industries and visual content creation.
Stable Diffusion
Frequently used for text-to-image generation and editing, popular in design, marketing, and content creation.
ResNet (Residual Networks)
A foundational model for image classification and object detection, used in many computer vision applications.
YOLO (You Only Look Once)
Popular in real-time object detection for security, surveillance, and autonomous vehicles.
Transformer
Foundational architecture for many NLP and GenAI models, including BERT, GPT, and T5.
GAN (Generative Adversarial Network)
Widely used for image, video, and audio generation, as well as synthetic data creation.
T5 (Text-To-Text Transfer Transformer)
Popular in NLP for translation, summarization, and question answering, similar to BERT and GPT.
VAE (Variational Autoencoder)
Commonly used in image generation, anomaly detection, and data reconstruction.
CLIP (Contrastive Language–Image Pretraining)
Well-regarded for multimodal applications, especially matching text to images and cross-modal search.
Faster R-CNN (Region-based Convolutional Neural Network)
A leading model in object detection, extensively used in image analysis and autonomous vehicles.
LSTM (Long Short-Term Memory)
Popular for sequence data like language modeling, speech synthesis, and time series prediction.
RNN (Recurrent Neural Network)
Common in sequence data tasks, although somewhat less used now due to advancements with transformers.
BART (Bidirectional and Auto-Regressive Transformers)
Popular for text generation, summarization, and machine translation, often used in chatbots.
XGBoost (Extreme Gradient Boosting)
A staple in structured data modeling, often used in predictive analytics and competitions.
AutoML (Automated Machine Learning)
Widely used to streamline model building and tuning, especially in companies with limited ML expertise.
StyleGAN
Renowned for creating realistic faces and objects, popular in entertainment and design.
Word2Vec
Often used for word embedding and semantic similarity, though less so as transformers have taken the lead.
EfficientNet
Popular for image classification, valued for its efficiency in resource-constrained environments.
SimCLR (Simple Framework for Contrastive Learning of Visual Representations)
Popular in self-supervised learning and representation learning, used in image pretraining.
AlphaGo
Famous for reinforcement learning in game environments, specifically complex strategy games.
UNet
Widely used in medical imaging segmentation, valuable for healthcare applications.
RoBERTa (Robustly Optimized BERT Approach)
A popular variant of BERT with improved performance, often used in language tasks.
DenseNet (Densely Connected Convolutional Network)
Known for image classification and object detection, though ResNet is more widely used.
Perceiver
Gaining popularity in cross-modal tasks, handling diverse data types, though newer to the scene.
Neural Turing Machine
An experimental model for memory-augmented tasks, more research-focused than broadly applied.
Deep Q-Network (DQN)
Primarily used in reinforcement learning research and gaming, like in robotics and virtual environments.
AlexNet
Historically significant in computer vision but largely replaced by more advanced architectures like ResNet.
XLNet
Once popular for NLP tasks, although it’s less widely used today compared to BERT and GPT models.
