我的作业代码仓库:https://github.com/Kodp/EECS498_Homework 隔壁CS231N课程主页:http://cs231n.stanford.edu/ 补充数学知识参考资料:https://www.researchgate.net/publication/322949882_The_Matrix_Calculus_You_Need_For_Deep_Learning 课程说明 计算机视觉已经在我们的社会中变得无处不在,应用程序包括搜索、图...
https://www.youtube.com/playlist?list=PL5-TkQAfAZFbzxjBHtzdVCWE0Zbhomg7r 课程名称:Deep Learning for Computer Vision 地址:https://web.eecs.umich.edu/~justincj/teaching/eecs498/ 李飞飞的博士生Justin Johnson到密歇根大学任教后,开设的这门课程,可以看做是斯坦福CS231N的升级版(斯坦福的课程在2017版...
学习LSTM,可以参考同样出自Stanford的cs224d,以及Colah的blog(Understanding LSTM Networks)。 同是在2016年,由Andrew Ng主办,在湾区的Conference:Bay Area Deep Learning School 2016也是值得推荐的佳品。 在工业上,运用Deep Learning并不是直接从零开始去写代码,而是运用现成的,主要由Google、Facebook等公司维护的开源...
新的Tutorial相比旧的Tutorial添加了Convolutional Neural Network的内容。了解的童鞋都知道CNN在Computer Vision的重大影响。 而且从新编排了内容及exercises。 新的UFLDL网址为: http://ufldl.stanford.edu/tutorial/ 2 Linear Regression 理论简述 对于线性回归Linear Regression,恐怕大部分童鞋都了解。简单的说 线性回归问...
Deep Learning 【2,3】 《Deep Learning 教程翻译》 介绍:是Stanford 教授 Andrew Ng 的 Deep Learning 教程,国内的机器学习爱好者很热心的把这个教程翻译成了中文。如果你英语不好,可以看看这个 《Deep Learning 101》 介绍:因为近两年来,深度学习在媒体界被炒作很厉害(就像大数据)。其实很多人都还不知道什么是深...
Stanford’s GloVe Model These distributed word representation models can be downloaded and incorporated into deep learning language models in either the interpretation of words as input or the generation of words as output from the model. In his book on Deep Learning for Natural Language Processing,...
In the Stanford course on deep learning for computer vision titled “CS231n: Convolutional Neural Networks for Visual Recognition” developed by Andrej Karpathy, et al., the Adam algorithm is againsuggested as the default optimization method for deep learning applications. ...
Stanford CS230: Deep Learning | Autumn 2018 | Lecture 1 – Class Introduction and Logistics Stanford CS230: Deep Learning | Autumn 2018 | Lecture 2 – Deep Learning Intuition Stanford CS230: Deep Learning | Autumn 2018 | Lecture 3 – Full-Cycle Deep Learning Projects Stanford...
curriculum and lifelong learning, where the problem requires learning a sequence of tasks, leveraging their shared structure to enable knowledge transfer Stanford CS330: Multi-Task and Meta-Learning, 2019 | Lecture 1 – Introduction & Overview ...
it came to images—wasn’t labeled, and that’s what you needed to train neural nets. That’s where Fei-Fei Li, a Stanford AI professor, stepped in. “Our vision was that big data would change the way machine learning works,” she explains in an interview. “Data drives learning.” ...