Introduction The main difference between the CPU version and the GPU version is the running speed; the GPU version runs faster. Therefore, if your computer’s graphics card supports CUDA, it is recommended to install the GPU version. The CPU version requires no additional preparation and can generally be installed on any computer without needing a … Read more
Click the above “Beginner Learning Vision” to choose to add Star Mark or “Top” Important content delivered immediately I recently started using PyTorch and have encountered quite a few pitfalls. I’m recording them here as I feel they are common issues that many might face. I would also like to thank my friends who helped … Read more
Source: Machine Heart Author: Jiang Siyuan The length of this article is 8300 words, recommended reading time is 8 minutes This article will start from the original paper, using Goodfellow’s speech at NIPS 2016 and Li Hongyi’s explanation from National Taiwan University, to complete the derivation, proof, and implementation of the original GAN. This article … Read more
Proofread by: Zhu Jianghua Feng To ensure the quality of our publications and establish a good reputation, Data Dispatch has establishedthe “Typo Fund”, encouragingreaders to actively report errors. If you find any errors while reading this article, please leave a comment at theend of the article, or provide feedback in thebackground. After confirmation by the … Read more
Currently, in the field of deep learning, Generative Adversarial Networks (GANs) are very popular, bringing us an incredible direction in this field. Today, I will share how to use GANs to generate images (Mnist and cartoon faces). 1. How GANs Came to Be It is said in academia that the founder of GANs, Ian Goodfellow, … Read more
Click on the top “Beginner’s Guide to Vision“, select to add “Star” or “Top“ Important content delivered promptly 01 Introduction to GAN Introduction Generative Adversarial Networks (GAN) is a generative model proposed by OpenAI researcher Ian Goodfellow in 2014. Since its introduction, it has received widespread attention and research in the field of deep learning. … Read more
Translator | Zhu Xianzhong Reviewer | Sun Shujuan This article will comprehensively explain what Generative Adversarial Networks (GANs) are, how they work, and how to build such a network in a Python environment. Recently, the data science community has been vigorously promoting Generative Adversarial Networks (GANs). However, as you begin to understand them, you will … Read more
In 2014, GoogLeNet and VGG were the two leading models in that year’s ImageNet competition (ILSVRC14), with GoogLeNet taking first place and VGG second. A common feature of these two model architectures is their increased depth. VGG inherits some structural elements from LeNet and AlexNet, while GoogLeNet made bolder structural attempts. Although it has only … Read more
Click on the top “Beginner’s Guide to Computer Vision”, and choose to add a star or “pin” Essential insights delivered in real time. In computer vision, CNNs are indispensable. However, what do convolution, pooling, and Softmax actually look like, and how are they interconnected? Imagining it from the code can be a bit daunting. Therefore, … Read more
Source: Big Data Talk Author: Huang Wenjian This article is 11200 words long and is recommended to be read in 15 minutes. This article explains some principles of convolutional neural networks (CNN) in deep learning and some classic network architectures. Overview of Convolutional Neural Network Principles Convolutional Neural Networks (CNN) were originally designed to solve … Read more