Convolution Operation: The Foundation of CNNs
Interactive guide to convolution in CNNs: visualize sliding windows, kernels, stride, padding, and feature detection with step-by-step demos.
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10 min read
ML Concepts - Page 7
Clear explanations of core machine learning concepts, from foundational ideas to advanced techniques. Understand attention mechanisms, transformers, skip connections, and more.
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Page 7
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Concepts - Page 7
Browse by categoryMatryoshka Embeddings
Matryoshka embeddings: nested representations enabling dimension reduction by simple truncation without model retraining for flexible retrieval.
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10 min read · intermediate
GPU Streaming Multiprocessor (SM)
How a single SM actually runs work: warps of 32, cutaway of cores and memory, divergence tax, latency-hiding occupancy, and coalesced loads — instruments, not a catalog.
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16 min read · intermediate
Pinned Memory and DMA Transfers in PyTorch
Why pin_memory=True matters: pageable paths pay two host copies and block the CPU; pinned memory enables one DMA hop and real overlap with GPU compute.
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10 min read · intermediate
Virtual Memory & TLB: Complete Guide to Address Translation
Master virtual memory and TLB address translation with interactive demos. Learn page tables, page faults, and memory management optimization.
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16 min read · advanced
ALiBi: Attention with Linear Biases
Learn ALiBi, the position encoding method that adds linear biases to attention scores for exceptional length extrapolation in transformers.
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12 min read · intermediate
Showing 37-42 of 194 concepts