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來自 deeplearning.ai 的課程

Neural Networks and Deep Learning

38112 個評分

deeplearning.ai

Neural Networks and Deep Learning

38112 個評分

課程 1（共 5 門，Specialization Deep Learning）

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.

從本節課中

Neural Networks Basics

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

Binary Classification8:23

Logistic Regression5:58

Logistic Regression Cost Function8:11

Gradient Descent11:23

Derivatives7:10

More Derivative Examples10:27

Computation graph3:33

Derivatives with a Computation Graph14:33

Logistic Regression Gradient Descent6:42

Gradient Descent on m Examples8:00

與講師見面

Andrew Ng
Co-founder, Coursera; Adjunct Professor, Stanford University; formerly head of Baidu AI Group/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

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 cos 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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