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Explore advanced LLM-powered competitive programming using RAG for code generation. Learn to design robust architectures, implement AST-based similarity search, and enhance few-shot learning for AI agents.
Explore DSPy's innovative approach to code generation, enhancing AI capabilities for developers and researchers in neural information processing systems.
Explore DSPy, a Stanford framework for optimizing language model pipelines in code generation. Learn its applications, potential, and hands-on usage with the HackerCup dataset.
Explore groundbreaking AI inference technology with Cerebras Systems CEO, discussing wafer-scale chips, architectural innovations, and the future of machine learning in production environments.
Strategies for fine-tuning large language models with limited resources: memory optimization, gradient accumulation, activation checkpointing, LoRa, and dataset quality. Practical advice for 40 GB VRAM GPU constraints.
Explore SWEAgent: AI tool for solving complex coding challenges. Learn setup, customization, and practical applications through live demonstrations and real-world examples.
Explore TorchRL and TensoDict for advanced reinforcement learning in PyTorch, covering RLHF, data handling, and practical applications for streamlined RL workflows.
Explore AI agents' impact on code generation and software development with Imbue CEO Kanjun Qiu. Learn about autonomous AI systems, practical research, and the challenges of creating AI that writes and verifies code.
Explore MLOps evolution in e-commerce, focusing on LLMs' impact, key investments, and real-world examples from Booking.com's AI implementation and scaling challenges.
Explore techniques to enhance LLM factual consistency using out-of-domain data and two-step fine-tuning. Learn to leverage Weights & Biases for model evaluation and improve hallucination detection.
Explore strategies for aligning LLM judges in evaluations, focusing on RAG pipelines. Learn to build effective systems using LlamaIndex and W&B Weave for systematic assessment and annotation.
Improve a Climate RAG chatbot for production using Weights & Biases Weave. Learn end-to-end process including tracing, benchmarking, and incorporating feedback for iterative enhancement.
Learn to use Weights & Biases Registry for versioning, tracking, and sharing ML models and datasets. Explore logging, managing artifacts, custom registries, and publishing models and datasets.
Discover how to build, evaluate, and monitor agentic AI workflows using W&B Weave through a practical hiring assistant that screens candidates with advanced tracing and guardrails.
Discover how DeepL's CEO built an AI translation powerhouse, competing with Google through neural networks, custom data centers, and enterprise-focused quality innovations.
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