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In the Math section of the Appendix, the numerator of the derivative of f(x) = g(x)/h(x) should be subtraction not addition.
h(x).g'(x) + g(x).h'(x) => h(x).g'(x) - g(x).h'(x)

Nice work Daniel.

I found a typo at line 60 of https://dafriedman97.github.io/mlbook/content/conventions_notation.html, where your second "is written as $$" is missing the empty line before the "$$".

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Congratulations on a releasing the well structured book! Neat use of Jupyter book!

I thought of use of no-constructor classes in your code - good for combining methods together,
but not really a great pattern for starting programming - not to replicate when a learner just starts writing Python code.

class LinearRegression:

    def fit(self, X, y, intercept = False):

        # record data and dimensions
        if intercept == False: # add intercept (if not already included)
            ones = np.ones(len(X)).reshape(len(X), 1) # column of ones 
            X = np.concatenate((ones, X), axis = 1)
        self.X = np.array(X)
        self.y = np.array(y)
        self.N, self.D = self.X.shape
        
        # estimate parameters
        XtX = np.dot(self.X.T, self.X)
        XtX_inverse = np.linalg.inv(XtX)
        Xty = np.dot(self.X.T, self.y)
        self.beta_hats = np.dot(XtX_inverse, Xty)
        
        # make in-sample predictions
        self.y_hat = np.dot(self.X, self.beta_hats)
        
        # calculate loss
        self.L = .5*np.sum((self.y - self.y_hat)**2)

I understand this is an idiom throughout the book, but if you consider restructuring the code at some point,
there can be a more functional representation:

        # estimate parameters
        XtX = np.dot(self.X.T, self.X)
        XtX_inverse = np.linalg.inv(XtX)
        Xty = np.dot(self.X.T, self.y)
        self.beta_hats = np.dot(XtX_inverse, Xty)

def estimate_betas(X,Y):
        XtX = np.dot(X.T, X)
        XtX_inverse = np.linalg.inv(XtX)
        Xty = np.dot(X.T, Y)
        return np.dot(XtX_inverse, Xty)

Correct me if I wrong her,e when i copy paste the whole code it gives me an error of invalid value encountered in matmul eventhough I copied the whole code and pasted on my notebook. Thank you in advance.

Hi,

I read your eBook on Machine Learning which very well explains everything about linear regression, neural networks etc. It's really helpful.
I however had a question on the neural network implementation in Python (https://dafriedman97.github.io/mlbook/content/c7/construction.html).

For simplicity, the model has only 1 layer between input and output as stated in the beginning of the text.
What about the following line in the code then? -> ffnn.fit(X_boston_train, y_boston_train, n_hidden = 8)
n_hidden is set at 8. Can you exaggerate on this? What does n_hidden mean exactly and why is it set at 8 in this example?

Kind regards,
Matthias

Your issue content here.

Your issue content here.

In "What Readers Should Know" section there is no speace between "math and probability" and "needed" words.

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