专栏

- 线性变化的几何表现 首先看下简单的矩阵，这是一个对角矩阵 M=(3001)\begin{gather*} M=\begin{pmatrix} 3& 0 \\ 0 & 1 \end{pmatrix} \end{gather*} 我们先用这个对角矩阵乘以一个点来看看它的几何变化。 (3001)∗(xy)=(3xy)\begin{gather*} \begin{pmatrix} 3& 0 \\

import numpy as np import theano.tensor as T import theano floatX='float32'V=T.vector('V') A=T.matrix('A') y=T.tanh(T.dot(V,A))results,updates=theano.scan(lambda i:T.grad(y[i],V),sequences=[T.arange(y.

import os import numpy as np import pandas as pd from sklearn.utils import shuffleFTRAIN='./input/training.csv' FTEST='./input/test.csv'df_train=pd.read_csv(FTRAIN) df_train['Image']=df_train['Image'].

""" network.py ~~~~~~~~~~ A module to implement the stochastic gradient descent learning algorithm for a feedforward neural network. Gradients are calculated using backpropagation. Note that I have f

import numpy as np import pandas as pd %matplotlib inline import matplotlib.pyplot as plt import matplotlib.cm as cmfrom lasagne.layers import Conv2DLayer from lasagne.layers import MaxPool2DLayer fr

1.关于梯度简单的理解f(x,y)=xy 可以很容易得到f(x,y)关于x 和y的偏导数 函数关于每个变量的偏导数告诉了你整个函数对于单个变量的敏感程度。2.链式法则f(x,y,z)=(x+y)z 可以把上面的公式分解成为 q=x+y 和 f=qz 对y求偏导也同样如此 # set some inputs x = -2; y = 5; z = -

import numpy import theano import theano.tensor as T rng = numpy.randomN = 400 # training sample size feats = 784 # number of input varia

#### Libraries # Standard library import gzip import pickle # Third-party libraries import numpy as np import theano import theano.tensor as T from theano.tensor.nnet import conv from theano.tensor.nne

from sklearn.ensemble import RandomForestClassifier import numpy as np import pandas as pd dataset=pd.read_csv('input/train.csv') test=pd.read_csv('input/test.csv')dataset.describe()

import pandas as pd import warnings #ignore the warnings that generated by seaborn warnings.filterwarnings('ignore') import seaborn as sns %matplotlib inline import matplotlib.pyplot as pltsns.set(s

# Imports# pandas import pandas as pd from pandas import Series,DataFrame# numpy, matplotlib, seaborn import numpy as np import matplotlib.pyplot as plt import seaborn as sns sns.set_style('whitegrid')

# Standard scientific Python imports %matplotlib inline import matplotlib.pyplot as plt# Import datasets, classifiers and performance metrics from sklearn import datasets, svm, metrics# The digits data

import pandas as pd import numpy as np import warnings #drop warnings generated by warnings.filterwarnings('ignore') import seaborn as sns %matplotlib inline import matplotlib.pyplot as plt sns.set(s

pca的目标是通过基变换 使得元素的方差从大到小排列 并且元素之间的协方差为0（线性无关）。而元素的协方差 矩阵的对角线上是元素的方差 ，对角线外的元素是协方差。 我们的目标就是 找到单位正交基，使得通过基变换后 ，元素的方差从大到小排列 而不同元素的协方差为0 。元素的协方差矩阵 和经过基变换后的协方差矩阵关系如下 原数据为X X的协方差矩阵为C Y=PX P为变换矩阵 Y为变换后的元