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Neural networks, cool?

2022-08-03 15:32:00 【I love computer vision】

Neural networks have been very popular in the past few years,I believe that everyone has a certain understanding of neural networks.Graph neural networks, on the other hand, perform computations directly on graphs,the whole calculation process,Follow the structure of the diagram,The advantage of this processing is that the structural information of the graph can be well preserved.Instead, structural information can be learned,This is the power of graph neural networks.

Let's take a look at why graph neural networks are powerful？

The application scenarios of graph neural networks are naturally very diverse.The author selects some application scenarios here to make a brief introduction for everyone,More is still looking forward to our joint discovery and exploration.

1. 计算机视觉

There are applications in computer visionGenerate an image based on the provided semantics,如下图所示（引用）.输入是一张语义图,GNN通过对“man behind boy on patio”和“man right of man throwing firsbee”两个语义的理解,生成了输出的图像.

当然还有动作识别、视觉问答等应用,这里我们就不一一列举了

2. 生物医疗

我们在高中都接触过生物化学,知道化合物是由原子和化学键构成的,它们天然就是一种图数据的形式,所以Graph neural networks are particularly widely used in the biomedical field.包括新药物的发现、化合物筛选、蛋白质相互作用点检测、以及疾病预测.

据笔者所知,目前国外包括耶鲁、哈佛,In China, like Peking University and Tsinghua University, there are many laboratories researching graph neural networks在医学方面的应用,而且我相信这会是图神经网络最有价值的应用方向之一

3. Recommended for industrial applications

推荐是机器学习在互联网中的重要应用.互联网业务中,推荐的场景特别说,比如内容推荐、电商推荐、广告推荐等等.这里,我们介绍三种图神经网络赋能推荐的方法.

Although it has attracted a lot of attention,But many challenges remain when applying it to other fields,From methodological rationale to actual commercial business performance.

So in order to let everyone better grasp the graph neural network, 「咕泡科技」特别邀请到「人工智能实战专家 - 唐宇迪博士」,专为深度学习的同学开设了「Graph Neural Network Architecture Bootcamp - <实战篇>」 ,既包括前沿的学术分享、又有来自头部大咖的实践分享.

图神经网络架构与实战训练营

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课程安排

上课时间：8月3日-4日,每晚20:00-22:30

课程服务：录播＋直播授课+讲师答疑+课堂笔记+作业布置

Day1：深度学习必备核心算法通俗解读

神经网络模型细节知识点分析.
神经网络模型整体架构解读.
计算机视觉核心模型-卷积神经网络.
卷积神经网络整体架构及其参数设计.

Day2：Ai最前沿-Graph Neural Networks in action

图神经网络在社交网络,Application analysis of scenarios such as biomedicine.
Graph neural network working details,How to use graph models to build inference models.
图神经网络框架Pytorch_geometric实战应用.
Analysis and summary of the latest research progress on graph models.

注：本次训练营有PPT课件、课堂笔记.
PPT课件、课堂笔记会在8月4日统一发给完成全部作业且2天都到课的同学.

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