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- LLM + VLM Integration: Multimodal AI Systems
Curriculum
- 9 Sections
- 70 Lessons
- Lifetime
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- 1. Large Language Model Deep Dive8
- 1.13T6D 1.1 Transformer Architecture in Detail – Attention Mechanisms
- 1.23T6D 1.2 LLM Training – Scaling Laws and Efficiency
- 1.33T6D 1.3 Tokenization and Vocabulary Design for Language
- 1.43T6D 1.4 Context Windows and Sequence Handling
- 1.53T6D 1.5 Advanced Prompting – Chain-of-Thought Reasoning
- 1.63T6D 1.6 LLM Inference Optimization and Quantization
- 1.73T6D 1.7 Adapters and Efficient Fine-Tuning – LoRA and QLoRA
- 1.83T6D 1.8 Popular LLM Architectures – Llama, Mistral, Qwen
- 2. Vision-Language Models Fundamentals8
- 2.13T6D 2.1 Vision Transformer Architecture (ViT) Explained
- 2.23T6D 2.2 Image Encoders – CNNs and ResNets for VLMs
- 2.33T6D 2.3 Visual Embeddings and Feature Extraction
- 2.43T6D 2.4 Cross-Modal Alignment – Vision and Language
- 2.53T6D 2.5 VLM Training Objectives – Contrastive Learning
- 2.63T6D 2.6 Popular VLM Models – CLIP, LLaVA, Qwen-VL
- 2.73T6D 2.7 Zero-Shot Vision Understanding Capabilities
- 2.83T6D 2.8 VLM Evaluation Metrics and Benchmarks
- 3. Multimodal Architecture and Design8
- 3.13T6D 3.1 Multimodal Fusion Strategies – Early, Mid, Late Fusion
- 3.23T6D 3.2 Cross-Attention Mechanisms for Modality Interaction
- 3.33T6D 3.3 Adapter Modules Bridging Vision and Language
- 3.43T6D 3.4 Attention Pooling for Image-to-Text Projection
- 3.53T6D 3.5 Modality Alignment and Synchronization
- 3.63T6D 3.6 Handling Variable-Length Inputs and Padding
- 3.73T6D 3.7 Memory-Efficient Architectures for Edge Devices
- 3.83T6D 3.8 Scaling Multimodal Systems Horizontally
- 4. Building Multimodal Systems8
- 4.13T6D 4.1 End-to-End Pipeline Design and Architecture
- 4.23T6D 4.2 Input Preprocessing for Multiple Modalities
- 4.33T6D 4.3 Unified Embedding Spaces and Representations
- 4.43T6D 4.4 Query Understanding – Visual and Textual
- 4.53T6D 4.5 Retrieval in Multimodal Space
- 4.63T6D 4.6 Response Generation with Multimodal Context
- 4.73T6D 4.7 Caching Strategies for Inference Optimization
- 4.83T6D 4.8 Error Handling and Fallback Mechanisms
- 5. Real-time Multimodal Processing8
- 5.13T6D 5.1 Streaming Video and Text Input Handling
- 5.23T6D 5.2 Frame Sampling and Temporal Modeling
- 5.33T6D 5.3 Batching Strategies for Mixed Modalities
- 5.43T6D 5.4 GPU Optimization for Multimodal Inference
- 5.53T6D 5.5 Latency-Throughput Trade-offs
- 5.63T6D 5.6 Asynchronous Processing Pipelines
- 5.73T6D 5.7 Buffering and Queue Management
- 5.83T6D 5.8 Monitoring Real-Time System Performance
- 6. Knowledge Integration Across Modalities8
- 6.13T6D 6.1 Knowledge Graph Integration with Multimodal Systems
- 6.23T6D 6.2 Fact Verification Across Text and Images
- 6.33T6D 6.3 Cross-Modal Reasoning and Logic
- 6.43T6D 6.4 Information Fusion from Heterogeneous Sources
- 6.53T6D 6.5 Semantic Consistency Between Modalities
- 6.63T6D 6.6 External Knowledge Bases with Multimodal Queries
- 6.73T6D 6.7 Handling Conflicting Information Across Modalities
- 6.83T6D 6.8 Chain of Reasoning for Complex Tasks
- 7. Fine-tuning Multimodal Models8
- 7.13T6D 7.1 Transfer Learning for Multimodal Systems
- 7.23T6D 7.2 Instruction Tuning for Visual Understanding
- 7.33T6D 7.3 Multi-Task Fine-Tuning Across Modalities
- 7.43T6D 7.4 Parameter-Efficient Fine-Tuning – Adapters
- 7.53T6D 7.5 Data Augmentation for Multimodal Training
- 7.63T6D 7.6 Loss Functions for Multimodal Objectives
- 7.73T6D 7.7 Evaluation and Validation During Training
- 7.83T6D 7.8 Avoiding Catastrophic Forgetting in Fine-Tuning
- 8. Production Deployment and Scaling7
- 8.13T6D 8.1 Containerization of Multimodal Systems with Docker
- 8.23T6D 8.2 Kubernetes Orchestration for Multimodal Services
- 8.33T6D 8.3 Load Balancing Multimodal Inference
- 8.43T6D 8.4 Distributed Inference Across GPUs
- 8.53T6D 8.5 Model Serving Frameworks – vLLM and Ray Serve
- 8.63T6D 8.6 Monitoring, Observability, and Logging
- 8.73T6D 8.7 Cost Optimization and Resource Allocation
- 9. Real-world Projects and Applications7
- 9.13T6D 9.1 Project – Document Understanding and Analysis
- 9.23T6D 9.2 Project – Visual Question Answering Systems
- 9.33T6D 9.3 Project – Intelligent Image Captioning Platform
- 9.43T6D 9.4 Project – Multimodal Search and Retrieval
- 9.53T6D 9.5 Project – Video Understanding and Summarization
- 9.63T6D 9.6 Project – Real-Time Visual Reasoning Chatbot
- 9.73T6D 9.7 Project – Accessibility Platform for Text-Image Understanding
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