CSCI-GA.3033-102 Special Topic - Learning with Large Language and Vision Models vs PostHog

Provides structured academic curriculum for teaching integration of large language models with vision models through hands-on projects and theoretical foundations. The course architecture combines lecture-based instruction with practical assignments that guide students through building systems that process and reason over both text and visual inputs simultaneously, using modern transformer-based architectures for cross-modal understanding.

Unique: Structured as a specialized graduate seminar focusing specifically on the intersection of LLMs and vision models rather than treating them as separate domains — curriculum design emphasizes architectural patterns for effective cross-modal fusion and alignment, with assignments building toward understanding both theoretical foundations and practical implementation constraints of multimodal systems.

vs alternatives: Provides university-backed rigorous curriculum with faculty expertise in multimodal learning, whereas most online resources treat vision and language models separately or focus on fine-tuning existing models rather than understanding architectural design principles for building integrated systems.

Delivers practical assignments and projects that require students to implement multimodal systems end-to-end, combining vision encoders (e.g., ViT, ResNet) with language model decoders through attention mechanisms and fusion layers. The pedagogical approach uses iterative project cycles where students build, evaluate, and refine implementations while receiving structured feedback on architectural choices, training stability, and cross-modal alignment quality.

Unique: Emphasizes architectural decision-making through comparative implementation — students don't just train models, they implement multiple fusion strategies and evaluate trade-offs empirically, building intuition about when early vs. late fusion or cross-attention mechanisms are appropriate for different multimodal tasks.

vs alternatives: Goes deeper than tutorial-based learning (which often provide pre-built models) by requiring students to implement core components and debug training instabilities, producing practitioners who understand multimodal system design rather than just API consumers.

Integrates reading and reproducing recent research papers on vision-language models as a core learning mechanism, where students analyze published architectures (CLIP, BLIP, LLaVA, etc.), understand the design rationale behind specific components, and implement simplified versions to verify claims. This capability combines literature review with hands-on reproduction, using paper-to-code mapping to bridge theoretical contributions and practical implementation details.

Unique: Treats paper reproduction as a primary learning mechanism rather than optional supplementary activity — curriculum explicitly maps published architectures to implementation patterns, helping students develop the skill of translating research contributions into working code and identifying which design choices are critical vs. implementation details.

vs alternatives: More rigorous than reading papers passively or using pre-built implementations — reproduction forces students to grapple with ambiguities and undocumented details, building deeper understanding of why specific architectural choices were made and their empirical impact.

Provides frameworks and assignments for analyzing learned embedding spaces where images and text are projected into a shared vector space, using dimensionality reduction (t-SNE, UMAP) and similarity metrics to visualize alignment quality. Students learn to diagnose multimodal model behavior by examining whether semantically similar image-text pairs cluster together and identifying failure modes where the embedding space is poorly aligned.

Unique: Emphasizes embedding space analysis as a primary diagnostic tool for multimodal model development — rather than treating embeddings as a black box, curriculum teaches students to interpret geometric structure, identify alignment failures, and use visualization to guide architectural improvements.

vs alternatives: More interpretable than relying solely on downstream task metrics (accuracy, BLEU) — embedding space analysis reveals whether alignment failures are due to poor representation learning vs. downstream task-specific issues, enabling more targeted debugging.

Teaches principles for building effective multimodal datasets by understanding image-text pairing strategies, annotation quality requirements, and dataset bias implications. Students learn to evaluate existing datasets (COCO, Flickr30K, Conceptual Captions) for their strengths and limitations, and design custom annotation pipelines for domain-specific multimodal tasks using crowdsourcing or semi-automated approaches.

Unique: Treats dataset design as a first-class architectural decision with implications for model behavior — curriculum emphasizes that multimodal model performance is bottlenecked by data quality and alignment strategy, not just model architecture, and teaches systematic approaches to dataset evaluation and construction.

vs alternatives: More comprehensive than simply using off-the-shelf datasets — teaches students to critically evaluate dataset suitability, understand annotation trade-offs, and design custom pipelines when needed, producing practitioners who can build high-quality multimodal systems rather than being limited to existing public data.

PostHog/posthog | DeepWiki Loading... Index your code with Devin DeepWiki DeepWiki PostHog/posthog Index your code with Devin Edit Wiki Share Loading... Last indexed: 28 May 2026 ( 4a5e38 ) Overview Monorepo Structure and Build System Frontend Workspace and Product Packages Python Dependencies and Configuration CI/CD Pipeline Schema and Type System Cross-Language Schema Synchronization Query Schema Definitions Database Migrations Data Storage and Ingestion ClickHouse Architecture Kafka to ClickHouse Pipeline PostgreSQL and Database Pools Query Log Archive System Event Ingestion Pipeline (Node.js) Backend Services Django Middleware System Feature Flags Service (Rust) API Layer and Authentication Rust Microservices LLM Gateway Service Agentic Provisioning and OAuth Max AI Assistant Architecture and Agent Modes Query Execution and Streaming Frontend Integration MCP Server Tasks (AI Coding Agent) Feature Flags System Feature Flag Management API Flag Evaluation and Dependencies Frontend Interface Product Features Logs Viewer Session Recordings Insights and Analytics Surveys and Scheduled Changes Experiments (A/B Testing) Web Analytics Error Tracking LLM Analytics Frontend Architecture Kea State Management Product Module System Build System and Tooling Testing and Quality Test Infrastructure Backend and Rust Tests Frontend and E2E Tests Data Platform and Workf

Monorepo Structure and Build System | PostHog/posthog | DeepWiki Loading... Index your code with Devin DeepWiki DeepWiki PostHog/posthog Index your code with Devin Edit Wiki Share Loading... Last indexed: 28 May 2026 ( 4a5e38 ) Overview Monorepo Structure and Build System Frontend Workspace and Product Packages Python Dependencies and Configuration CI/CD Pipeline Schema and Type System Cross-Language Schema Synchronization Query Schema Definitions Database Migrations Data Storage and Ingestion ClickHouse Architecture Kafka to ClickHouse Pipeline PostgreSQL and Database Pools Query Log Archive System Event Ingestion Pipeline (Node.js) Backend Services Django Middleware System Feature Flags Service (Rust) API Layer and Authentication Rust Microservices LLM Gateway Service Agentic Provisioning and OAuth Max AI Assistant Architecture and Agent Modes Query Execution and Streaming Frontend Integration MCP Server Tasks (AI Coding Agent) Feature Flags System Feature Flag Management API Flag Evaluation and Dependencies Frontend Interface Product Features Logs Viewer Session Recordings Insights and Analytics Surveys and Scheduled Changes Experiments (A/B Testing) Web Analytics Error Tracking LLM Analytics Frontend Architecture Kea State Management Product Module System Build System and Tooling Testing and Quality Test Infrastructure Backend and Rust Tests Frontend a

Schema and Type System | PostHog/posthog | DeepWiki Loading... Index your code with Devin DeepWiki DeepWiki PostHog/posthog Index your code with Devin Edit Wiki Share Loading... Last indexed: 28 May 2026 ( 4a5e38 ) Overview Monorepo Structure and Build System Frontend Workspace and Product Packages Python Dependencies and Configuration CI/CD Pipeline Schema and Type System Cross-Language Schema Synchronization Query Schema Definitions Database Migrations Data Storage and Ingestion ClickHouse Architecture Kafka to ClickHouse Pipeline PostgreSQL and Database Pools Query Log Archive System Event Ingestion Pipeline (Node.js) Backend Services Django Middleware System Feature Flags Service (Rust) API Layer and Authentication Rust Microservices LLM Gateway Service Agentic Provisioning and OAuth Max AI Assistant Architecture and Agent Modes Query Execution and Streaming Frontend Integration MCP Server Tasks (AI Coding Agent) Feature Flags System Feature Flag Management API Flag Evaluation and Dependencies Frontend Interface Product Features Logs Viewer Session Recordings Insights and Analytics Surveys and Scheduled Changes Experiments (A/B Testing) Web Analytics Error Tracking LLM Analytics Frontend Architecture Kea State Management Product Module System Build System and Tooling Testing and Quality Test Infrastructure Backend and Rust Tests Frontend and E2E Tests

PostHog/posthog | DeepWiki Loading... Index your code with Devin DeepWiki DeepWiki PostHog/posthog Index your code with Devin Edit Wiki Share Loading... Last indexed: 28 May 2026 ( 4a5e38 ) Overview Monorepo Structure and Build System Frontend Workspace and Product Packages Python Dependencies and Configuration CI/CD Pipeline Schema and Type System Cross-Language Schema Synchronization Query Schema Definitions Database Migrations Data Storage and Ingestion ClickHouse Architecture Kafka to ClickHouse Pipeline PostgreSQL and Database Pools Query Log Archive System Event Ingestion Pipeline (Node.js) Backend Services Django Middleware System Feature Flags Service (Rust) API Layer and Authentication Rust Microservices LLM Gateway Service Agentic Provisioning and OAuth Max AI Assistant Architecture and Agent Modes Query Execution and Streaming Frontend Integration MCP Server Tasks (AI Coding Agent) Feature Flags System Feature Flag Management API Flag Evaluation and Dependencies Frontend Interface Product Features Logs Viewer Session Recordings Insights and Analytics Surveys and Scheduled Ch