Artículo: AMZ-B0F4YMMS7H

Supercomputing for Artificial Intelligence: Foundations, Architectures, and Scaling Deep Learning Workloads

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En stock
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1.48 kg
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Amazon

Sobre este producto
  • The foundations of supercomputing and its role in AI workloads
  • Practical GPU programming with CUDA and distributed systems
  • Parallel programming with MPI on modern clusters
  • Efficient training of neural networks, CNNs, and Transformers
  • Performance optimization for deep learning at scale
  • Distributed training with PyTorch DistributedDataParallel (DDP)
  • Building and scaling LLMs using real biomedical and NLP datasets
  • Jupyter, Google Colab, and Hugging Face workflows
  • Deployment and inference strategies for modern LLMs
  • Instructors looking for practical material for AI and HPC courses
  • Students and professionals wanting to learn how to run AI at scale
  • Engineers transitioning from standard AI workflows to distributed environments and seeking system-level judgments
  • Researchers working on LLMs and interested in reproducible pipelines
  • 800+ pages of real-world content tested in supercomputing classrooms
  • Hands-on examples with PyTorch, CUDA, MPI, and SLURM
  • Full GitHub access with ready-to-run scripts and datasets
  • Workflows adapted for Google Colab, and HPC clusters
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$173,72
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