<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>ForeverYoung</title><description>Driving into the distance — notes on ML, CUDA, Python, and beyond.</description><link>https://foreveryounggithub.github.io/</link><item><title>Data Visualization with Hand-Drawn/Sketchy Style</title><link>https://foreveryounggithub.github.io/en/posts/sketchy_rendering/</link><guid isPermaLink="true">https://foreveryounggithub.github.io/en/posts/sketchy_rendering/</guid><description>A survey of tools for creating hand-drawn/sketchy style data visualizations: rough.js, draw.io, matplotlib xkcd, chart.xkcd, and cutecharts.</description><pubDate>Tue, 19 Apr 2022 00:00:00 GMT</pubDate></item><item><title>Model Size vs. Inference Speed in Deep Learning</title><link>https://foreveryounggithub.github.io/en/posts/model_speed_vs_size/</link><guid isPermaLink="true">https://foreveryounggithub.github.io/en/posts/model_speed_vs_size/</guid><description>An examination of how FLOPs, parameter count, memory access volume, and memory footprint affect inference speed, with practical network design recommendations for different hardware platforms.</description><pubDate>Fri, 04 Mar 2022 13:27:08 GMT</pubDate></item><item><title>Learning Strategies for Patch-Based Local Descriptors</title><link>https://foreveryounggithub.github.io/en/posts/patch_based_local_descriptor/</link><guid isPermaLink="true">https://foreveryounggithub.github.io/en/posts/patch_based_local_descriptor/</guid><description>A survey of data processing and training strategies for learned image local descriptors, focusing on what distinguishes different approaches to patch-based learning.</description><pubDate>Sun, 27 Feb 2022 17:12:44 GMT</pubDate></item><item><title>pybind11: Python Bindings for C++/CUDA Code</title><link>https://foreveryounggithub.github.io/en/posts/python_cpp_extension/</link><guid isPermaLink="true">https://foreveryounggithub.github.io/en/posts/python_cpp_extension/</guid><description>Using pybind11 to create Python bindings for C++/CUDA code, with zero-copy conversion between numpy/torch tensors and Eigen/cv::Mat.</description><pubDate>Sun, 20 Sep 2020 00:00:00 GMT</pubDate></item><item><title>Numba: Accelerating Python Code with Simple Decorators</title><link>https://foreveryounggithub.github.io/en/posts/numba_python/</link><guid isPermaLink="true">https://foreveryounggithub.github.io/en/posts/numba_python/</guid><description>An introduction to using numba&apos;s decorators for just-in-time compilation to speed up Python functions — easy to use, flexible, and effective.</description><pubDate>Sat, 04 Jul 2020 00:00:00 GMT</pubDate></item><item><title>Preprocess the Image Data by NPP in TensorRT Model Inference</title><link>https://foreveryounggithub.github.io/en/posts/trt_preproc_npp/</link><guid isPermaLink="true">https://foreveryounggithub.github.io/en/posts/trt_preproc_npp/</guid><description>How to use NVIDIA NPP library to accelerate image preprocessing (uint8→float32) for TensorRT model inference pipelines.</description><pubDate>Wed, 17 Jun 2020 00:00:00 GMT</pubDate></item><item><title>Numba: Learning CUDA Programming Quickly with Python</title><link>https://foreveryounggithub.github.io/en/posts/numba_cuda/</link><guid isPermaLink="true">https://foreveryounggithub.github.io/en/posts/numba_cuda/</guid><description>A fast introduction to CUDA&apos;s multithreaded, highly concurrent programming model using Python&apos;s numba library, designed to lower the barrier for beginners.</description><pubDate>Wed, 10 Jun 2020 00:00:00 GMT</pubDate></item></channel></rss>