等待13年，AlexNet重磅开源：Hinton团队亲手写的原版代码，甚至还带注释

Core Insights - AlexNet's original source code has been open-sourced after 13 years, allowing AI developers and deep learning enthusiasts to access the foundational code that revolutionized computer vision [1][10][11] - The release includes the original 2012 version written by Geoffrey Hinton's team, complete with annotations, providing insights into the development process of deep learning models [1][11] Group 1: Historical Context - AlexNet emerged in 2012 during the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), significantly reducing the Top-5 error rate from 26.2% to 15.3%, marking a pivotal moment in computer vision [2][3] - Prior to AlexNet, neural networks faced skepticism and were largely overlooked due to limitations in computational power and data availability, with a resurgence occurring in the 1980s following the rediscovery of the backpropagation algorithm [4][6] Group 2: Technical Aspects - AlexNet consists of 5 convolutional layers and 3 fully connected layers, totaling 60 million parameters and 650,000 neurons, utilizing GPU acceleration for training [2][3] - The success of AlexNet was facilitated by the availability of the ImageNet dataset, which was crowdsourced and became the largest image dataset at the time, and advancements in GPU technology, particularly NVIDIA's CUDA programming system [5][6] Group 3: Development and Impact - The open-sourcing of AlexNet's code was a collaborative effort between the Computer History Museum and Google, taking five years to navigate licensing complexities [10][11] - AlexNet's publication has led to over 170,000 citations, establishing it as a seminal work in the field of deep learning and influencing subsequent research and development in AI [7][10]