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Computation graph

Learn to set up a machine learning problem with a neural network mindset. Learn to use vectorization to speed up your models.

Neural Networks and Deep Learning

deeplearning.ai

課程 1（共 5 門，深度学习專項課程）

If you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new "superpower" that will let you build AI systems that just weren't possible a few years ago. In this course, you will learn the foundations of deep learning. When you finish this class, you will: - Understand the major technology trends driving Deep Learning - Be able to build, train and apply fully connected deep neural networks - Know how to implement efficient (vectorized) neural networks - Understand the key parameters in a neural network's architecture This course also teaches you how Deep Learning actually works, rather than presenting only a cursory or surface-level description. So after completing it, you will be able to apply deep learning to a your own applications. If you are looking for a job in AI, after this course you will also be able to answer basic interview questions. This is the first course of the Deep Learning Specialization.

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您將學習的技能

Artificial Neural Network, Backpropagation, Python Programming, Deep Learning

教學方

Andrew Ng
CEO/Founder Landing AI; Co-founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist,Baidu and founding lead of Google Brain
Head Teaching Assistant - Kian Katanforoosh
Lecturer of Computer Science at Stanford University, deeplearning.ai, Ecole CentraleSupelec
Teaching Assistant - Younes Bensouda Mourri
Mathematical & Computational Sciences, Stanford University, deeplearning.ai

腳本

You've heard me say

that the computations

of a neural network are organized

in terms of a forward pass

or a forward propagation step,

in which we compute the output

of the neural network,

followed by a backward pass

or back propagation step,

which we use to compute gradients

or compute derivatives.

The computation graph explains

why it is organized this way.

In this video, we'll go through

an example.

In order to illustrate

the computation graph,

let's use a simpler example

than logistic regression

or a full blown neural network.

Let's say that we're trying to compute a function, J,

which is a function

of three variables a, b, and c

and let's say that function is 3(a+bc).

Computing this function actually

has three distinct steps.

The first is you need to compute

what is bc

and let's say we store that

in the variable call u.

So u=bc and then you my compute V=a *u.

So let's say this is V.

And then finally, your output J is 3V.

So this is your final function J

that you're trying to compute.

We can take these three steps

and draw them in a computation graph as follows.

Let's say, I draw your three variables

a, b, and c here.

So the first thing we did

was compute u=bc.

So I'm going to put

a rectangular box around that.

And so the input to that are b and c.

And then, you might have V=a+u.

So the inputs to that

are V. So the inputs to that are u

with just computed together with a.

And then finally, we have J=3V.

So as a concrete example, if a=5,

b=3 and c=2 then u=bc would be six

because a+u would be 5+6 is 11,.

J is three times that, so J=33.

And indeed, hopefully you can verify

that this is three times five

plus three times two.

And if you expand that out,

you actually get 33 as the value of J.

So, the computation graph comes in handy

when there is some distinguished

or some special output variable,

such as J in this case,

that you want to optimize.

And in the case

of a logistic regression,

J is of course the cost function

that we're trying to minimize.

And what we're seeing

in this little example is that,

through a left-to-right pass,

you can compute the value of J

and what we'll see

in the next couple of slides

is that in order to compute derivatives

there'll be a right-to-left

pass like this,

kind of going in the opposite direction

as the blue arrows.

That would be most natural

for computing the derivatives.

So to recap, the computation graph

organizes a computation with this blue arrow,

left-to-right computation.

Let's refer to the next video

how you can do the backward red arrow

right-to-left computation

of the derivatives.

Let's go on to the next video.

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