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models that create
learn generative ai
gans, diffusion, and large language models — the theory and the code to ship.
the curated path
curatedintermediate~5 weeks, part-time
generative ai foundations
how machines learn to create — gans, diffusion, and llms — paired with the canonical code repos so you can generate, not just read.
4 modules · 12 resources · checkpoint per modulemore generative ai paths
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what's new in generative ai
- MAP: evaluation and multi-agent enhancement of large language models for inpatient pathwaysevaluates and improves LLMs for complex inpatient decision-making by moving beyond question-answering to real clinical workflows. practitioners need this because it addresses the gap between medical benchmarks and the messy, multi-step reasoning required in actual hospital settings.
- PopuLoRA: Co-Evolving LLM Populations for Reasoning Self-Playuses population-based self-play with LoRA adapters to improve reasoning in LLMs through reinforcement learning with verifiable rewards. practitioners benefit because it shows how to efficiently fine-tune models for complex reasoning tasks without full retraining.
- TabPFN-3: Technical Reportscales foundation models for tabular data to handle 1M+ rows while reducing training and inference time significantly. this is critical for practitioners because most real-world prediction problems use tabular data, and this removes a major bottleneck in deploying models on large datasets.
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