PyTorch occupies an important position in the field of deep learning. In real life, it is widely used in various areas such as image recognition and natural language processing. For example, in medical image diagnosis, models built with PyTorch can quickly and accurately identify lesions in X-ray and CT images; in intelligent customer service systems, … Read more
PyTorch Lightning Bolts is an extension library officially launched by PyTorch Lightning, aiming to accelerate model development and training. It provides commonly used deep learning components, pre-trained models, data loaders, and utilities that can significantly simplify the development process, allowing developers to focus on research and optimizing model performance. Whether you are a beginner or … Read more
Selected from efficientdl.com Author: LORENZ KUHN Translated by: Machine Heart Editor: Chen Ping Master these 17 methods to accelerate your PyTorch deep learning training with minimal effort. Recently, a post on Reddit gained immense popularity. The topic was about how to speed up PyTorch training. The original author is LORENZ KUHN, a master’s student in … Read more
1. Introduction: The AI Wave Is Here In today’s rapidly advancing technological era, generative artificial intelligence is undoubtedly the most shining star. From ChatGPT, which can create stories effortlessly, to Midjourney, which can draw exquisite paintings, and AI voice synthesis tools that realistically reproduce human voices, they are infiltrating every aspect of our lives at … Read more
In the previous article: Implementing the Classic CNN VGGNet (Theoretical Part) We detailed the network structure of VGGNet. Today, we will use PyTorch to reproduce the VGGNet network and apply the VGGNet model to solve a classic Kaggle image recognition competition problem. Let’s get started! 1. Dataset Preparation In the paper, the authors of AlexNet … Read more
Background of Deep Learning: In March of this year, Google’s AlphaGo emerged and defeated South Korean player Lee Sedol, causing a frenzy in the fields of deep learning and machine learning, not only in academia but also among the general public. Artificial intelligence has had its ups and downs, but currently, the hottest topic is … Read more
This article mainly summarizes the notes from the CS231n course, adding knowledge from the Deep Learning Book and practical TensorFlow, as well as the Caffe framework. 1. Convolutional Neural Networks 1.1 Convolutional Neural Networks vs. Conventional Neural Networks 1.1.1 Similarities Convolutional networks are a type of neural network specifically designed to process data with a … Read more
1. Faster R-CNN Model The R-CNN series networks are the most classic networks in the field of object detection, and their model update ideas are easy to understand. The object detection process is divided into three stages: candidate box generation, feature extraction, classification, and regression. R-CNN is a detection network assembled from many modules, where … Read more
Hello everyone, I am Red Stone! The first typical CNN is the LeNet5 network, while the first CNN to shine brightly was AlexNet. In 2012, AlexNet emerged in the globally renowned image recognition competition ILSVRC, directly reducing the error rate by nearly 10 percentage points, which was something all previous machine learning models could not … Read more
↓↓↓Click to follow, reply withmaterials, a 10GB surprise Author: zzq Editor: Jishi Platform Source: https://zhuanlan.zhihu.com/p/68411179 For academic sharing only, does not represent the position of this publicaccount. Contact for removal if infringing. Introduction to Basic Components of CNN 1. Local Receptive Field In images, the connections between local pixels are relatively tight, while the connections … Read more