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13 articles
Learn how Amazon is challenging Nvidia's dominance in AI chips by creating a tool that helps developers use their own chips with the popular PyTorch programming language.
Learn to build and train GPU-accelerated neural networks using NVIDIA's CUDA platform and PyTorch, demonstrating the performance advantages that explain NVIDIA's projected 70% growth.
Learn to implement core components of modern AI architectures like linear hybrids, compressed indexers, and gated residuals using Python and PyTorch.
Learn how to set up and use NVIDIA GPU computing with Python and PyTorch to accelerate AI development. This beginner-friendly tutorial teaches you to create neural networks that utilize GPU acceleration for faster performance.
Learn to accelerate transformer training using NVIDIA's Transformer Engine with fused kernels, FP8, and BF16 optimizations in PyTorch.
Learn to build and train world model architectures using PyTorch, understanding the technology behind AMI Labs' approach to AI development.
Learn to build a simplified world model inspired by China's Orca system that predicts abstract world states without requiring labeled action data, demonstrating how unsupervised learning can reduce data requirements in robotics.
Learn to implement key Transformer architecture components including attention mechanisms and multi-head attention using PyTorch, replicating the technology behind OpenAI's successful AI systems.
Learn to implement a simplified version of MiniMax's Sparse Attention mechanism that reduces computational complexity in attention operations while maintaining performance.
Learn how to implement and experiment with the Aurora optimizer that fixes neuron death problems in neural network training.
An open-source project called OpenMythos attempts to reconstruct Anthropic's Claude Mythos architecture from first principles, achieving 1.3B-level performance with only 770M parameters through advanced modeling techniques.
Learn how to use SymTorch to convert simple neural networks into human-readable mathematical equations, making deep learning models more interpretable.
