A little a little depth knowledge of learning

Depth study on the first day

Our hands have a lot of x and y, find the weight
training is weighted
data samples

x	W	Y
height	0
body weight	0
blood group	0.3
Toes	0.8

A group of x to calculate a Y corresponding
calculation is w
following picture is the most basic formula
we know a lot of data x and y are
desired to be obtained by W "Sitar"

Our study has only just begun, you first need to understand that the door

x1	x2	Y
0	0	0
0	1	0
1	0	0
1	1	1

Above is a AND gate, where the principle of the brain will be long, so that we directly on the code

def AND(x1, x2):
    w1, w2, theta = 0.5, 0.5, 0.7
    tmp = x1*w1 + x2*w2
    if tmp <= theta:
        return 0
    elif tmp > theta:
        return 1

Here let us consider the NAND and OR gates

NAND gate

x1	x2	Y
0	0	1
0	1	1
1	0	1
1	1	0

OR

x1	x2	Y
0	0	0
0	1	1
1	0	1
1	1	1

Simple implementation of machine perception

After understanding the above gate, we began to implement a simple Perceptron
just with the door code is a fundamental part of the knowledge, let's make some changes to their implementation

Here we used this formula

import  numpy as np
x = np.array([0,1])
w = np.array([0.5,0.5])
b = -0.7
print(np.sum(w*x))
print(np.sum(w*x)+b)
#0.5
#-0.19999999999999996

From this we can see that, when two identical numpy array elements, we adding and multiplying them (i.e. -numpy.sum () method)
again and b (offset) added to complete computing the above formula

Then we realized just an AND gate, where we first posted the first AND gate

def AND(x1, x2):
    w1, w2, theta = 0.5, 0.5, 0.7
    tmp = x1*w1 + x2*w2
    if tmp <= theta:
        return 0
    elif tmp > theta:
        return 1
#0 0 0 1        

Then we want to achieve with the new door

import  numpy as np
def AND(x1,x2):
    w = np.array([0.5,0.5])
    x = np.array([x1,x2])
    b = -0.7
    tmp = np.sum(w*x)+b
    if tmp <= 0:
        return 0
    elif tmp > 0:
        return 1
#0 0 0 1        

Specifically, and W2 is W1 of the parameter control input signal of the importance of , and offset (b) is adjusted neuron is activated easiness (a degree of the output signal) of the parameters .

Then we realize NAND and OR gates

import numpy as np
def NAND(x1,x2):
    w = np.array([-0.5,-0.5])#仅权重和偏置改变了
    x = np.array([x1,x2])
    b = 0.7
    tmp = np.sum(w*x)+b
    if tmp <=0:
        return 0
    elif tmp >0:
        return 1

def OR(x1, x2):
    x = np.array([x1, x2])
    w = np.array([0.5, 0.5]) # 仅权重和偏置与AND不同！
    b = -0.2
    tmp = np.sum(w*x) + b
    if tmp <= 0:
        return 0
    else:
        return 1
# 1 1 1 0
# 0 1 1 1

AND gates, NAND gates, OR gates perceptron having the same configuration, except that only the value of the weight parameter. Thus, achieving NAND gate and an OR gate, the values ​​of the weights and bias provided only that the gate and a different implement.

XOR gates are special

def XOR(x1,x2):
    s1 = NAND(x1,x2)
    s2 = OR(x1,x2)
    y = AND(s1,s2)
    return y
    # 0 1 1 0

But there are digital logic-based, it should not be difficult to understand

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Let me talk about these, and the rest destined goodbye

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