Multimodal AI Systems
From CLIP to GPT-4V — build models that see, hear, and read
4 phases. 14 lessons. 14 labs. 1 capstone. Multimodal AI from vision-language models to end-to-end systems — multimodal foundations (CLIP, BLIP, image-text alignment), vision-language models (LLaVA, GPT-4V architecture, visual instruction tuning), multimodal generation (text-to-image, image editing, audio-visual generation), and production multimodal systems (RAG with images, multimodal agents, evaluation & deployment). You build a multimodal AI system that processes text, images, and audio toge
- Lessons: —
- Labs: —
- Projects: —
- Level: Beginner
Curriculum
- Multimodal Foundations — Processing multiple modalities, fusion strategies, and alignment.
- Vision-Language Models — CLIP, Flamingo, and vision-language pretraining.
- Audio-Visual Models — Audio-visual fusion, video understanding, and multimodal generation.
- Multimodal Generation — Text-to-image, text-to-video, and multimodal generation.
- Multimodal Deployment — Serving multimodal models, optimization, and real-world applications.
Skills You Will Learn
- CLIP & Contrastive Image-Text Learning
- BLIP & BLIP-2 for Vision-Language
- Cross-Modal Retrieval
- LLaVA Architecture (Vision Encoder + LLM)
- Visual Instruction Tuning
- GPT-4V-Style Multimodal Reasoning
- Multimodal Generation (Text-to-Image, Image Editing)
- Multimodal RAG
- Multimodal Agents
- Multimodal System Evaluation
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