刘老师讲的十分细节，易于理解，大家可以去学习，课堂地址，废话不多说，直接上代码。
梯度下降算法课堂代码:

# 梯度下降算法

import matplotlib.pyplot as plt
x_data = [1.0,2.0,3.0]
y_data = [2.0,4.0,6.0]

w = 1.0

def forward(x):
    return x * w

#计算损失函数MSE
def cost(xs,ys):
    cost = 0
    for x,y in zip(xs,ys):
        y_pred = forward(x)
        cost += (y_pred - y) ** 2
    return cost / len(xs)

#计算梯度
def gradient(xs,ys):
    grad = 0
    for x,y in zip(xs,ys):
        grad += 2 * x * (x * w - y)
    return grad / len(xs)

print('Predict (before training)',4,forward(4))

epoch_list = []
cost_list = []
#进行一百轮的训练
for epoch in range(100):
    cost_val = cost(x_data,y_data)
    grad_val = gradient(x_data,y_data)
    w -= 0.01 * grad_val
    print('Epoch:',epoch,'w = ',w,'loss = ',cost_val)
    epoch_list.append(epoch)
    cost_list.append(cost_val)

print('Predict (after training)',4,forward(4))
plt.plot(epoch_list,cost_list)
plt.xlabel('Epoch')
plt.ylabel('Cost')
plt.grid()
plt.show()

结果图如下：
　　　　　　　　　　　　　　　
　　　　　　　　　　　　

随机梯度下降算法：

基本思路：只通过一个随机选取的数据 ( x n , y n ) (x_n,y_n) (xn​,yn​) 来获取“梯度”，以此对 ω \omega ω 进行更新，这种优化方法叫做随机梯度下降。

#随机梯度下降

import matplotlib.pyplot as plt

import matplotlib.pyplot as plt
x_data = [1.0,2.0,3.0]
y_data = [2.0,4.0,6.0]

w = 1.0

def forward(x):
    return x * w

#计算损失函数MSE
def loss(x,y):
    y_pred = forward(x)
    return (y_pred - y) ** 2

#计算梯度
def gradient(x,y):
    return 2 * x * (x * w - y)

print('Predict (before training)',4,forward(4))

epoch_list = []
loss_list = []
#进行一百轮的训练
for epoch in range(100):
    for x,y in zip(x_data,y_data):
        grad = gradient(x,y)
        w -= 0.01 * grad
        print('\tgrad: ',x,y,grad)
        l = loss(x,y)

    print('progress:',epoch,'w = ',w,'loss = ',l)
    epoch_list.append(epoch)
    loss_list.append(l)
print('Predict (after training)',4,forward(4))
plt.plot(epoch_list,loss_list)
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.grid()
plt.show()

运行结果图：