Tks for any help

The first rule is: always trust the error. If the error says â€śNot all tests passed for L1. Check your equationâ€¦â€ť Go ahead and check that equation. You have to implement the below equation:

\begin{align*} & L_1(\hat{y}, y) = \sum_{i=0}^{m-1}|y^{(i)} - \hat{y}^{(i)}| \end{align*}

First, you have to find the **absolute** difference between y and y-hat and then sum all the values.

Hint:

Use `abs`

and `np.sum`

Best,

Saif.

I do not understand your hints

I used for L1

loss = np.sum(y, dtype=float)

and it gives me the global variable error

And I used for L2

loss = np.sum(np.abs(yhat-y), axis=0)

and it gives me the global variable error

Why you are using this? Can you please explain? And, is it same as equation of `L1`

given to you?

You mean this for L1

loss = np.sum(np.abs(yhat-y), axis=0)

and for L2 the square?

Yes, but we donâ€™t need to mention any axis. You can skip that.

But it is `y - yhat`

.

And for L2?

loss= np.dot(np.abs(yhat,y)

L2 doesnâ€™t require the `abs`

but you need to square the difference. Formula is:

\begin{align*} & L_2(\hat{y},y) = \sum_{i=0}^{m-1}(y^{(i)} - \hat{y}^{(i)})^2 \end{align*}

In this formula of L1 where can I read the np.abs ?

Which is the sign that indicates the absolute value the Summatory?

How can I distinguish from L1 and L2 formulas when I have to use the absolute values?

DIdi I make myself understood?

In the below formula

\begin{align*} & L_1(\hat{y}, y) = \sum_{i=0}^{m-1}|y^{(i)} - \hat{y}^{(i)}| \end{align*}

These two lines indicate the abs: || . This is just a basic mathematical notation. If you are not aware of the basic math, you should do this specialization, before any Machine or Deep learning.

Furthermore, if you are new to Python, you should learn the basics of Python before taking any of these courses.

It was simply resolved by a single explanation as You do right nowâ€¦

Hi,

Just to piggy-back on this:

I understand we can use the following to calculate the L2 norm:

loss = np.sum((y-yhat)**2)

But how do we use the np.dot() function as stated in the question?

Nothing I tried intuitively worked when trying to use np.dot() to solve this.

What does dot product do? It takes two vectors of the same size and then multiplies each of the corresponding elements of the two vectors. Then it adds up the products to get a scalar value which is sum.

In our case what we want is the sum of the squares of the elements of a vector, right? So what if we â€śdottedâ€ť it with itself? Iâ€™m talking about the â€śdifferenceâ€ť vector of course.