Dense Embeddings
How dense embeddings turn meaning into geometry: word2vec, GloVe, and contextual models, vector arithmetic, cosine similarity, and where the field is heading.
Clear explanations grouped by domain. Start with a category.
Attention mechanisms, large language models, and multimodal architectures: the building blocks of modern AI.
Neural network fundamentals: normalization, convolutions, losses, graph networks, and training dynamics.
Object detection, feature pyramids, and visual recognition techniques.
Dense and sparse embeddings, quantization, and vector search for semantic retrieval.
CUDA, tensor cores, multi-GPU communication, and cluster-scale workload orchestration.
CPU pipelines, memory hierarchies, the Linux kernel, filesystems, and networking.
How CPython, C++, and PyTorch work under the hood: bytecode, memory, linking, and concurrency.
How dense embeddings turn meaning into geometry: word2vec, GloVe, and contextual models, vector arithmetic, cosine similarity, and where the field is heading.
How the Calinski-Harabasz index evaluates clustering quality by measuring the ratio of between-cluster to within-cluster variance — fast, intuitive, and ideal for k-selection with convex clusters.
Complete guide to Slurm — architecture, core commands, job lifecycle, job scripts, array jobs, dependencies, monitoring with squeue/sacct, and troubleshooting failed jobs on HPC clusters.
Explore CPython bytecode compilation from source to .pyc files. Learn the dis module, PVM stack operations, and Python 3.11+ adaptive specialization.
PyTorch DataLoader deep dive — Dataset, Sampler, Workers, Collate internals, num_workers throughput profiling, memory analysis, serialization costs, production patterns (LMDB, WebDataset), and bottleneck diagnosis.
Deep dive into C++ memory allocation — stack frame internals, heap allocator mechanics, fragmentation, performance benchmarks, custom allocators, RAII, and debugging with AddressSanitizer and Valgrind.