说明:Generative Adversarial Networks
Throughout most of this book, we have talked about how to make predictions. In some form or another, we used deep neural networks learned mappings from data points to labels. This kind of learning is called discrimina <qq_40441895> 上传 | 大小:6kb
说明:we introduced the basic ideas behind how GANs work. We showed that they can draw samples from some simple, easy-to-sample distribution, like a uniform or normal distribution, and transform them into samples that appear to match the distribution of so <qq_40441895> 上传 | 大小:9kb
说明:Sparse Bayesian Learning and the Relevance Vector Machine
Michael E. Tipping
2001年的一篇早期资料,论述了贝叶斯框架下的回归与分类问题,并且结合了相关向量机方法进行学习。对于我们今天学习了解贝叶斯理论,SVM,依然有指导作用。 <cauchy> 上传 | 大小:936kb