当前位置:网站首页>Deep learning, "grain and grass" first--On the way to obtain data sets
Deep learning, "grain and grass" first--On the way to obtain data sets
2022-08-04 06:21:00 【language】
Event address: CSDN 21-day Learning Challenge
Tuesday August 2nd, 2022 partly cloudy
Creative Project
Opportunity: Record the growth of Deep Learning Xiaobai
Expectation: At least 21 days/article output to share deep learning related notes
Vision: To be able to keep writing and keep updating
| time | Theme | Content | Progress |
|---|---|---|---|
| 2022-08-01 | Hello world,Hello CNN MNIST! | Deep learning cloud environment experience, understand the deep learning framework | Completed |
| 2022-08-02 | Deep learning, "grass and grass" first - Talking about the way to obtain data sets | Master the way to obtain data sets and understand the processing methods of data sets in the deep learning framework | Completed |
| 2022-08-03 | Late night learning, just for "volume" - a detailed introduction to convolutional neural networks | Take weather recognition and clothing image classification as examples to explain convolutional neural networks | To learn |
Data
As early as the CCF-GAIR 2020 summit, Professor Zhihua Zhou pointed out in his report titled "Abductive Learning" that the three elements for the role of artificial intelligence technology - data, algorithms, and computing power, in previous yearsIn the "big data era", big data itself does not necessarily mean great value. Data is a resource. To obtain the value of resources, effective data analysis must be carried out, and effective data analysis mainly relies on machine learning algorithms. Machine learning algorithmsIn China, deep learning technology has made great progress and exerted great power with the support of big data and large computing power.

Today, data is stillA prerequisite for AI development,
"To be continued, looking forward to reunification..."
边栏推荐
- [Deep Learning 21-Day Learning Challenge] 3. Use a self-made dataset - Convolutional Neural Network (CNN) Weather Recognition
- MNIST手写数字识别 —— Lenet-5首个商用级别卷积神经网络
- MNIST handwritten digit recognition, sorted by from two to ten
- 详解近端策略优化
- 在AWS-EC2中安装Minikube集群
- Copy Siege Lion 5-minute online experience MindIR format model generation
- latex-写论文时一些常用设置
- 图像形变(插值方法)
- Linear Regression 02---Boston Housing Price Prediction
- 动手学深度学习_多层感知机
猜你喜欢

动手学深度学习_多层感知机

MNIST Handwritten Digit Recognition - Lenet-5's First Commercial Grade Convolutional Neural Network

Use of double pointers

【论文阅读】Anchor-Free Person Search
![[Deep Learning Diary] Day 1: Hello world, Hello CNN MNIST](/img/06/6f49260732e5832edae2ec80aafc99.png)
[Deep Learning Diary] Day 1: Hello world, Hello CNN MNIST

Amazon Cloud Technology Build On-Amazon Neptune's Knowledge Graph-Based Recommendation Model Building Experience

Pytorch语义分割理解

Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions

剪映专业版字幕导出随笔

深度学习理论——过拟合、欠拟合、正则化、优化器
随机推荐
Various commands such as creating a new user in postgresql
【CV-Learning】Object Detection & Instance Segmentation
Copy攻城狮的年度之“战”|回顾2020
浅谈外挂常识和如何防御
MNIST手写数字识别 —— ResNet-经典卷积神经网络
YOLOV5 V6.1 详细训练方法
DRA821 环境搭建
The second official example analysis of the MOOSE platform - about creating a Kernel and solving the convection-diffusion equation
典型CCN网络——efficientNet(2019-Google-已开源)
MNIST手写数字识别 —— Lenet-5首个商用级别卷积神经网络
MFC读取点云,只能正常显示第一个,显示后面时报错
【CV-Learning】图像分类
Endnote编辑参考文献
动手学深度学习__数据操作
投稿相关
2020-10-19
学习资料re-id
详解近端策略优化
【CV-Learning】线性分类器(SVM基础)
MFC 打开与保存点云PCD文件