tutorial about a simple model and how this applies to more complex ml model

Really like this example here:


	# Initializes parameters "a" and "b" randomly
	np.random.seed(42)
	a = np.random.randn(1)
	b = np.random.randn(1)

	print(a, b)

	# Sets learning rate
	lr = 1e-1
	# Defines number of epochs
	n_epochs = 1000

	for epoch in range(n_epochs):
	# Computes our model's predicted output
	yhat = a + b * x_train

	# How wrong is our model? That's the error!
	error = (y_train - yhat)
	# It is a regression, so it computes mean squared error (MSE)
	loss = (error ** 2).mean()

	# Computes gradients for both "a" and "b" parameters
	a_grad = -2 * error.mean()
	b_grad = -2 * (x_train * error).mean()

	# Updates parameters using gradients and the learning rate
	a = a - lr * a_grad
	b = b - lr * b_grad

	print(a, b)

	# Sanity Check: do we get the same results as our gradient descent?
	from sklearn.linear_model import LinearRegression
	linr = LinearRegression()
	linr.fit(x_train, y_train)
	print(linr.intercept_, linr.coef_[0])

mitzen