English learning

简介: 1.Strong recommend:   http://www.englishclub.com/   http://www.talkenglish.com/   http://www.putclub.
1.Strong recommend:
2.Reading:
   http://www.cdlponline.org/  (Audlt learning)
   http://www.usalearns.org (Recommend)
   http://public.wsu.edu/~brians/errors/errors.html#errors (Common errors in English usage)

3.News:

4.Dictionary:
5.Other:
6.All of english website summary:
目录
相关文章
|
机器学习/深度学习 算法 决策智能
【5分钟 Paper】Deep Reinforcement Learning with Double Q-learning
【5分钟 Paper】Deep Reinforcement Learning with Double Q-learning
|
11月前
|
自然语言处理 数据挖掘 数据处理
【提示学习】Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference
目前流行的第四大范式Prompt的主流思路是PVP,即Pattern-Verbalizer-Pair,主打的就是Pattern(模板)与Verbalizer(标签映射器)。   本文基于PVP,提出PET与iPET,但是关注点在利用半监督扩充自己的数据集,让最终模型学习很多样本,从而达到好效果。
|
机器学习/深度学习 存储 人工智能
大语言模型的预训练[3]之Prompt Learning:Prompt Engineering、Answer engineering、Multi-prompt learning、Training strategy详解
大语言模型的预训练[3]之Prompt Learning:Prompt Engineering、Answer engineering、Multi-prompt learning、Training strategy详解
大语言模型的预训练[3]之Prompt Learning:Prompt Engineering、Answer engineering、Multi-prompt learning、Training strategy详解
|
自然语言处理 数据挖掘
【论文解读】Do Prompts Solve NLP Tasks Using Natural Language?
提示学习实现文本分类的各类方法对比的论文
87 0
|
机器学习/深度学习 移动开发 数据挖掘
Understanding Few-Shot Learning in Computer Vision: What You Need to Know
Few-Shot Learning is a sub-area of machine learning. It’s about classifying new data when you have only a few training samples with supervised information. FSL is a rather young area that needs more research and refinement. As of today, you can use it in CV tasks. A computer vision model can work
168 0
Understanding Few-Shot Learning in Computer Vision: What You Need to Know
|
机器学习/深度学习 传感器 数据挖掘
Review on the Recent Welding Research with Application of CNN-Based Deep Learning
Guo等人16)将CNN应用于线管制造过程中的电阻焊,提出了一种正常焊缝与缺陷焊缝的分类模型,准确率达到99.01%。
98 0
|
程序员 Go
Learn English -- Being late
个人学习笔记
843 0
Deep Learning vs. Machine Learning vs. Pattern Recognition
Deep learning, machine learning, and pattern recognition are highly relevant topics commonly used in the field of robotics with artificial intelligence.
4533 0