Clear explanations of core machine learning concepts, from foundational ideas to advanced techniques. Understand attention mechanisms, transformers, skip connections, and more.
How sched/backfill works — the algorithm that lets small jobs run in gaps while large jobs wait, why accurate time limits matter, and the key tuning parameters (bf_interval, bf_window, bf_max_job_test).
Understand CPython garbage collection: reference counting, generational GC for circular references, weak references, and gc module tuning strategies.
Explore ext4, the default Linux filesystem with journaling, extents, and proven reliability. Learn how ext4 protects your data.
Matryoshka embeddings: nested representations enabling dimension reduction by simple truncation without model retraining for flexible retrieval.
NVIDIA vs AMD for deep learning compared at both layers: the CUDA vs ROCm software moat, the microarchitecture (warp vs wavefront, SM vs CU, Tensor vs Matrix Cores), and the datacenter accelerators (H100/H200/B200 vs MI300X/MI325X).
Learn a profiler-first Python optimization workflow: measure bottlenecks, choose the right lever, and verify performance changes.