Artificial neural networks (ANN) are computational systems that “learn” to perform tasks by considering examples, generally without being programmed with any task-specific rules.
深度学习框架的核心能力不在于理论完备性，而在于能否支撑稳定、可复现、可调试的工程化训练流程。PyTorch凭借其动态图机制和细粒度控制成为工业界首选，但其自由度也带来显著上手门槛——典型如训练循环中梯度清零时机错误导致loss震荡，或DataLoader参数配置失当引发OOM与卡顿。理解autograd的 ...
算子是AI计算的原子单位，代表数学运算在硬件上的第一道映射；AI Infra则是支撑大模型训练与推理的底层系统工程，涵盖CUDA kernel开发、图编译优化、分布式通信调度及GPU内存管理。其核心原理在于突破传统深度学习的通用抽象，转向对硬件特性的深度编程——如A100的L2缓存行宽、H100的Tensor Core精度 ...
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