Deep L-layer neural network

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Do curso por deeplearning.ai

Neural Networks and Deep Learning

36584 classificações

deeplearning.ai

Neural Networks and Deep Learning

36584 classificações

Curso 1 de 5 em 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.

Na lição

Deep Neural Networks

Understand the key computations underlying deep learning, use them to build and train deep neural networks, and apply it to computer vision.

Deep L-layer neural network5:50

Forward Propagation in a Deep Network7:15

Getting your matrix dimensions right11:09

Why deep representations?10:33

Building blocks of deep neural networks8:33

Forward and Backward Propagation10:29

Parameters vs Hyperparameters7:16

What does this have to do with the brain?3:17

Conheça os instrutores

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

Welcome to the fourth week of this course.

By now, you've seen four promulgation and back promulgation in the context

of a neural network, with a single hidden layer, as well as logistic regression, and

you've learned about vectorization, and

when it's important to initialize the ways randomly.

If you've done the past couple weeks homework, you've also implemented and

seen some of these ideas work for yourself.

So by now,

you've actually seen most of the ideas you need to implement a deep neural network.

What we're going to do this week, is take those ideas and put them together so

that you'll be able to implement your own deep neural network.

Because this week's problem exercise is longer,

it just has been more work, I'm going to keep the videos for

this week shorter as you can get through the videos a little bit more quickly, and

then have more time to do a significant problem exercise at then end, which I hope

will leave you having thoughts deep in neural network, that if you feel proud of.

So what is a deep neural network?

You've seen this picture for logistic regression and

you've also seen neural networks with a single hidden layer.

So here's an example of a neural network with two hidden layers and

a neural network with 5 hidden layers.

We say that logistic regression is a very "shallow" model,

whereas this model here is a much deeper model, and

shallow versus depth is a matter of degree.

So neural network of a single hidden layer,

this would be a 2 layer neural network.

Remember when we count layers in a neural network, we don't count the input layer,

we just count the hidden layers as was the output layer.

So, this would be a 2 layer neural network is still quite shallow,

but not as shallow as logistic regression.

Technically logistic regression is a one layer neural network,

we could then, but over the last several years the AI,

on the machine learning community, has realized that there are functions that

very deep neural networks can learn that shallower models are often unable to.

Although for any given problem, it might be hard to predict in advance exactly how

deep in your network you would want.

So it would be reasonable to try logistic regression, try one and

then two hidden layers, and view the number of hidden layers as another hyper

parameter that you could try a variety of values of, and

evaluate on all that across validation data, or on your development set.

See more about that later as well.

Let's now go through the notation we used to describe deep neural networks.

Here's is a one, two, three, four layer neural network,

With three hidden layers, and the number of units in these hidden

layers are I guess 5, 5, 3, and then there's one one upper unit.

So the notation we're going to use,

is going to use capital L ,to denote the number of layers in the network.

So in this case, L = 4, and so does the number of layers, and

we're going to use N superscript [l] to denote the number of nodes,

or the number of units in layer lowercase l.

So if we index this, the input as layer "0".

This is layer 1, this is layer 2, this is layer 3, and this is layer 4.

Then we have that, for example, n[1], that would be this,

the first is in there will equal 5, because we have 5 hidden units there.

For this one, we have the n[2],

the number of units in the second hidden layer

is also equal to 5, n[3] = 3, and

n[4] = n[L] this number of upper units is 01,

because your capital L is equal to four,

and we're also going to have here that for

the input layer n[0] = nx = 3.

So that's the notation we use to describe the number of nodes we have in different

layers.

For each layer L, we're also going to use

a[l] to denote the activations in layer l.

So we'll see later that in for propagation,

you end up computing a[l] as the activation g(z[l]) and

perhaps the activation is indexed by the layer l as well,

and then we'll use W[l ]to denote, the weights for

computing the value z[l] in layer l, and

similarly, b[l] is used to compute z [l].

Finally, just to wrap up on the notation, the input features are called x,

but x is also the activations of layer zero, so a[0] = x,

and the activation of the final layer, a[L] = y-hat.

So a[L] is equal to the predicted output to prediction y-hat to the neural network.

So you now know what a deep neural network looks like,

as was the notation we'll use to describe and to compute with deep networks.

I know we've introduced a lot of notation in this video, but if you ever forget

what some symbol means, we've also posted on the course website, a notation sheet or

a notation guide, that you can use to look up what these different symbols mean.

Next, I'd like to describe what forward propagation in this type of network

looks like.

Let's go into the next video.

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