A complete walkthrough of implementing the original Attention Is All You Need encoder-decoder Transformer—no torch. nn.Transformer, no shortcuts. The 2017 paper "Attention Is All You Need" by Vaswani ...
You moved your model to the GPU. You watched nvidia-smi climb toward 100%. You assumed you were done. You probably aren’t. GPU utilization is a coarse, 100ms-sampled metric. A GPU can report 80% ...
A from-scratch PyTorch implementation of TurboQuant (ICLR 2026), Google's two-stage vector quantization algorithm for compressing LLM key-value caches — enhanced with a comprehensive, research-grade ...
Semantic segmentation is a core task in computer vision, essential for applications requiring detailed scene understanding, such as medical imaging, precision agriculture, and remote sensing. Recent ...
This tutorial will walk you through using PyTorch to implement a Neural Collaborative Filtering (NCF) recommendation system. NCF extends traditional matrix factorisation by using neural networks to ...
Monocular depth estimation involves predicting scene depth from a single RGB image—a fundamental task in computer vision with wide-ranging applications, including augmented reality, robotics, and 3D ...
TorchGeo is a Python package for integrating geospatial data into the PyTorch deep learning ecosystem, making it easy for machine learning and remote sensing experts to use geospatial data in their ...
Today, we are proud to announce DeepSpeed MoE, a high-performance system that supports massive scale mixture of experts (MoE) models as part of the DeepSpeed (opens in new tab) optimization library.
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