// Course · stage 7 of 9
Multimodal & Generative AI
Vision Transformers, CLIP and VLMs, diffusion and flow matching, speech, video and world models.
4 unitsabout 38 hours39 algorithms
01Units in this stage
In order. Each unit has goals, algorithms, must-read papers, a project and a self-test.
02Algorithms you will meet
Algorithms Atlas →Vision Transformer (ViT)SimCLRMasked Autoencoders (MAE)Joint-Embedding Predictive Architecture (JEPA)Segment Anything (SAM)Contrastive Learning (InfoNCE)U-NetFaster R-CNN (Object Detection)YOLO (Real-Time Object Detection)DETR (Detection Transformer)Swin TransformerDINOv2 (Self-Supervised Visual Features)CLIP (Contrastive Language–Image Pretraining)SigLIP (Sigmoid Loss)Visual Instruction Tuning (LLaVA)FlamingoVariational Autoencoder (VAE)Generative Adversarial Network (GAN)VQ-VAE (Neural Discrete Representation)Denoising Diffusion Probabilistic Models (DDPM)DDIMScore-Based Models via SDEsLatent Diffusion ModelsClassifier-Free Guidance (CFG)Diffusion Transformer (DiT)Flow MatchingRectified FlowConsistency ModelsControlNetVisual Autoregressive Modeling (VAR)Normalizing Flows (RealNVP / Glow)WhisperNeRF & 3D Gaussian SplattingWorld ModelsDreamer (Latent Imagination)Genie (Generative Interactive Environments)Connectionist Temporal Classification (CTC)wav2vec 2.0VALL-E (Neural Codec Language Model TTS)
Part of these routes: Algorithm Researchers · Domain Scientists