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Published inTowards Data ScienceDe-Coded: Understanding Context Windows for Transformer ModelsEverything you need to know about how context windows affect Transformer training and usageJan 271Jan 271
Published inTowards Data ScienceDe-coded: Transformers explained in plain EnglishNo code, maths, or mention of Keys, Queries and ValuesOct 9, 20238Oct 9, 20238
Published inTowards Data ScienceA Pythonista’s Intro to Semantic KernelIn this blog post, I shall demonstrate how to get started with the Semantic Kernel using Python.Sep 2, 20231Sep 2, 20231
Published inTowards Data ScienceYOLOv7: A deep dive into the current state-of-the-art for object detectionEverything you need to know to use YOLOv7 in custom training scriptsNov 25, 20229Nov 25, 20229
Published inTowards Data ScienceDemystifying PyTorch’s WeightedRandomSampler by exampleA straightforward approach to dealing with imbalanced datasetsAug 30, 20226Aug 30, 20226
Published inTowards Data ScienceSlicing images into overlapping patches at runtimeAn efficient approach to slicing high-resolution images for computer vision tasksJun 20, 20222Jun 20, 20222
Published inTowards Data ScienceTransfer Learning on Greyscale Images: How to Fine-Tune Pretrained Models on Black-and-White…Everything you need to know to understand why the number of channels matters and how to work around thisFeb 24, 20223Feb 24, 20223
Published inTowards Data ScienceGetting Started with PyTorch Image Models (timm): a practitioner’s guideThe purpose of this guide is to explore PyTorch Image Models (timm) from a practitioner’s point of view, for use in custom scripts.Feb 1, 202210Feb 1, 202210