Clear explanations of core machine learning concepts, from foundational ideas to advanced techniques. Understand attention mechanisms, transformers, skip connections, and more.
Complete guide to PyTorch pin_memory — how DMA transfers work, when pinning helps vs hurts, NUMA effects, profiling with torch.profiler, num_workers interaction, and debugging slow data loading.
Complete guide to C++ symbol resolution — how linkers match references to definitions, name mangling, strong vs weak symbols, ODR, template instantiation, linking order, and debugging undefined reference errors.
Learn how filesystem journaling prevents data loss during crashes. Explore write-ahead logging and recovery in ext4 and XFS.
Understand Linux inodes - the metadata structures behind every file. Learn about hard links, soft links, and inode limits.
Master sequential vs strided memory access patterns. Learn how cache efficiency and hardware prefetching affect application performance.
How a transformer’s per-token outputs become one embedding: CLS, mean, max, last-token, and attention pooling — what each does and when to use it.