Superscript

Superscript

Superscript

Superscript

Insert superscript

I and X superscript

So, in this notation, the superscript 2 here.

In order to distinguish between these different parameter vectors, I'm going to use a superscript 1, superscript 2 there where theta superscript 1 just means the parameters

The inverse is shown like this a negative 1 in the superscript.

So I'm also going to use X superscript

let me just denote this as theta superscript 1.

So this superscript i over here, this is not exponentiation right?

The mathematical notation for this is M superscript T, where T means transpose.

So concretely, this a superscript 2 subscript 1 does the activation of the first unit in layer 2, in our hidden layer.

Concretely, this means that for every example i I have a feature vector X superscript

I'm going to use a superscript j subscript i to denote the activation of neuron i or of unit i in layer j.

So I said in the beginning, this kind of is a squared operator. and why do they write it as a superscript there?

I write it superscript so there is no confusion with the index i, which is the grid cell.

This (x(i), y(i)), the superscript i in parentheses that's just an index into my training set and refers to the ith row in this table, okay?

This is a transpose which is written and A superscript T, and the way you compute the transpose of a matrix is as follows.

And you notice that I've written this with a superscript 2 here because theta superscript 2 is the matrix of parameters, or the matrix of weights that controls the function that maps from the hidden units, that is the layer 2 units, to the 1 layer 3 unit that is the output unit.

So, recall that a superscript I subscript j that does the activation of the j of unit in layer l and so, this delta term is in some sense going to capture our error in the activation of that neural duo.

Remember, that the parameters in the the neural network of these things, theta superscript I subscript ij, that's the real number and so, these are the partial derivative terms we need to compute.

In addition, our neural network is parametrized by these matrices, theta superscript j where our theta j is going to be a matrix of waves controlling the function mapping from one layer, maybe the first

The intuition of the back propagation algorithm is that for each note we're going to compute the term delta superscript I subscript j that's going to somehow represent the error of note j in the layer l.

I get by fitting this model to my training data, and theta superscript 2 just means the parameters I get by fitting this quadratic function to my training ata and so on.

Concretely, we're going to first take our first hypothesis, take this first model and say, minimize the cos function, and this would give me some parameter vector theta for the linear model and as before I'm going to put the superscript 1 just to denote that this is a parameter for the linear model.

I've been in India, in China, in Japan, in Australia. Everywhere.

Come in. Come in.

In April in Geneva,

In Kenya, in August,

Fall in! Fall in!

I work in Morocco, in Turkey, in Mexico.

PCBs accumulate in dolphins in Sarasota Bay, in Texas, in North Carolina.

That if you are a leader in any area in business, in politics, in sports, in art, in academics, in any area you don't own it.

In presence and in absence. In simplicity and complexity.

living in the Middle East in particular, in Iraq.

In 1700, in 1800, in 1900, no one cared.

What's in fashion in Paris?

Yes, come in, come in.

In contrast, in the unsupervised

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Well, come in, come in.

In that moment in time,

And in school, in Japan,