sinat_41498648的博客

对于观察到的样本集{(xi,yi)}\{(x_i,y_i)\}{(xi​,yi​)},i=1,...,N.xi∈Rn,yi∈{0,1}i=1,...,N.x_i\in\R^n,y_i\in\{0,1\}i=1,...,N.xi​∈Rn,yi​∈{0,1}，设这组数据的分布情况是满足二项逻辑斯蒂分布的，然后对他进行参数估计。设P(Y=1∣x)=ewx1+ewx=π(x)P(Y=1|x)=\frac{e^{wx}}{1+e^{wx}}=\pi(x)P(Y=1∣x)=1+ewxewx​=π(x)P(Y=0∣x

SVM推导至于对于非线性的时候核函数的使用，将Ki,jK_{i,j}Ki,j​换成几个常用的核函数试试就行了。不用LaTeX编辑公式就是快，不管了，反正写给自己看的，就这样吧^ - ^主要参考，李航的统计学习方法，以及自己的一些理解，可能会有偏差。...

莫烦pytorch学习记录--cnn#cnn.pyimport osimport timeimport numpy as npimport torchimport torchvisionimport torch.nn as nnfrom torch.utils.data import DataLoaderfrom torch.utils import dataimport torch.utils.data as Datafrom torch.autograd import Va

莫烦pytorch，集中优化方式的比较#optimizer.pyimport torchimport torch.nn.functional as Fimport torch.utils.data as Dataimport matplotlib.pyplot as pltfrom torch.autograd import Variable#hyper parametersLR=0.01BATCH_SIZE=32EPOCH=40x=torch.unsqueeze(torch.

学习莫烦pytorch视频，部分函数写法进行更新import torchimport torch.utils.data as DataBATCH_SIZE=8x=torch.linspace(1,10,10)y=torch.linspace(10,1,10)torch_dataset = Data.TensorDataset(x, y)#新版本直接用元组，(data_tensor=x, target_tensor=y)loader = Data.DataLoader( d

学习莫烦pytorch视频，训练好的网络进行保存和读取#qd.py#classification.pyimport numpy as npimport torchimport torch.nn.functional as Ffrom torch.autograd import Variableimport matplotlib.pyplot as pltimport mathimport pdbtorch.manual_seed(1)#CPU设置种子用于生成随机数，随机值确

学习莫烦pytorch视频，部分代码进行注释#classification.pyimport numpy as npimport torchimport torch.nn.functional as Ffrom torch.autograd import Variableimport matplotlib.pyplot as pltimport mathimport pdbn_data = torch.ones(100,2)#pdb.set_trace()x0 = torch.

学习莫烦的pytorch视频，对一些函数进行搜索，记录功能以及测试import torchimport numpy as npfrom torch.autograd import Variableimport torch.nn.functional as Fimport matplotlib.pyplot as pltimport pdbx=torch.unsqueeze(torch.linspace(-1,1,100),dim=1)#增加dim个维数，类似 int a=0==>int