Advanced NLP: Training & Production Systems
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Advanced NLP: Training & Production Systems
Master the engineering and production aspects of Natural Language Processing with interactive visualizations. Learn to train, fine-tune, optimize, and deploy language models at scale using modern techniques like LoRA, DPO, Flash Attention, and RAG. Features hands-on tools for exploring distributed training, model parallelism, and inference optimization.
Learning Objectives
- Master training fundamentals with interactive parallelism visualizations
- Implement advanced fine-tuning methods including PEFT, LoRA, and QLoRA
- Design preference alignment systems with RLHF and DPO
- Optimize inference with quantization, Flash Attention, and KV caching
- Build production RAG systems with embeddings and vector databases
- Deploy and monitor language models in production environments
Интерактивные инструменты в этом курсе
Осваивайте концепции через практическое изучение
Optimization Techniques Explorer
visualizationComprehensive tool for exploring optimization techniques
Transformer Architecture Explorer
visualizationComprehensive tool for exploring transformer architectures
Tokenization Workbench
visualizationComprehensive tool for exploring tokenization techniques
Course Content
Training Fundamentals and Optimization
Learn about dataset preparation, distributed training approaches, and optimization techniques for language models.
Training Monitoring and Dataset Engineering
Understand key metrics for monitoring model training, and learn techniques for dataset preparation, enhancement, and quality filtering.
Distributed Training Infrastructure
Learn about frameworks and approaches for distributed training, including DeepSpeed and FSDP, along with monitoring techniques.
Fine-tuning Techniques and Parameter-Efficient Methods
Master approaches for efficiently fine-tuning large language models, including PEFT methods like LoRA and QLoRA.
Preference Alignment and RLHF
Explore methods for aligning model outputs with human preferences, including DPO, PPO, and other alignment approaches.
Comprehensive Model Evaluation
Learn about automated benchmarks, human evaluation protocols, and model-based evaluation approaches for NLP systems.
Model Quantization and Compression
Understand techniques for model quantization, from basic approaches to advanced methods like GGUF, GPTQ, and AWQ.
Inference Optimization Strategies
Learn about techniques for optimizing model inference, including flash attention, KV caching, and speculative decoding.
Production RAG Systems
Build sophisticated RAG systems with chunking strategies, embeddings, rerankers, and vector databases for production deployment.
Advanced Model Implementations
Dive into practical implementation details, optimization techniques, and deployment strategies for cutting-edge models like LLaMA, Mixtral, Mistral, and Claude.
Production Deployment and Operations
Learn comprehensive strategies for deploying LLMs in production, including A/B testing, monitoring, scaling, and managing model versions.
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