Multimodal AI

What you'll learn

• Represent images, audio, video and documents as tensors a model can consume.
• Explain ViT patch embeddings, audio encoders and early, late and intermediate fusion.
• Describe contrastive training in CLIP and use shared embeddings for zero-shot classification and retrieval.
• Explain how vision-language models connect a vision encoder to an LLM and how they are trained.
• Describe diffusion-based image generation, text conditioning and speech generation pipelines.
• Build multimodal RAG, document extraction and video QA with hosted APIs, Hugging Face and local models.
• Control token cost, evaluate quality, and defend against hallucination, prompt injection and privacy risks.

Syllabus

Foundations and Mental Model

1. What Multimodal AI Means
2. Prerequisites: Tensors, Embeddings and Transformers
3. Fusion Strategies: Early, Late and In-Between

Representing Images, Audio and Video

1. Images as Tensors
2. Audio as Waveforms and Spectrograms
3. Video, Documents and Other Inputs

Encoders: Turning Signals into Tokens

1. Vision Encoders: CNNs and Vision Transformers
2. Patch Embeddings Step by Step
3. Audio Encoders and Speech Models

Contrastive Learning and CLIP

1. Shared Embedding Spaces
2. Contrastive Training: How CLIP Learns
3. Zero-Shot Classification and Retrieval

Vision-Language Models

1. Vision-Language Model Architecture
2. Cross-Attention and Other Connectors
3. How VLMs Are Trained

Generating Images and Speech

1. Diffusion Models for Image Generation
2. Text Conditioning, Latent Diffusion and Guidance
3. Speech Generation and Voice Interfaces

Using Multimodal Models in Practice

1. Sending Images to a Model API
2. Open Models with Hugging Face
3. Running Multimodal Models Locally

Applications: Multimodal RAG, Documents and Video

1. Multimodal Retrieval-Augmented Generation
2. Document AI: OCR, Layout and VLMs
3. Video Understanding Applications

Performance, Cost and Evaluation

1. Image Tokens, Resolution and Cost
2. Evaluating Multimodal Systems
3. Serving Fast and Cheap

Security, Safety and Pitfalls

1. Visual Hallucination and Grounding
2. Prompt Injection Through Images and Audio
3. Privacy, Bias and Synthetic Media

Revision and Interview Prep

1. Cheat Sheet: Multimodal AI in One Page
2. Common Interview and Exam Questions
3. Mixed Self-Check

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