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Network in sentence

1605 examples of Network in a sentence

It contains more than 50 million people and also has plans for a high-speed rail network.
It has plans for a rail network that will make it the anchor of a vast Atlantic coastal corridor, stretching across Benin, Togo and Ghana, to Abidjan, the capital of the Ivory Coast.
They know that their cities belong as much to the global network civilization as to their home countries.
In North America, the lines that matter most on the map are not the US-Canada border or the US-Mexico border, but the dense network of roads and railways and pipelines and electricity grids and even water canals that are forming an integrated North American union.
We are the global network civilization, and this is our map.
We could also see that she communicated a lot with many different people throughout the day and that she had a strong support network.
Think of it as a peer-to-peer payment network, like Bitcoin, but for governments.
The data is then sent on a cellular network to well-equipped hospitals hundreds of miles away for analysis.
We know that underneath our facial skin, there's a rich network of blood vessels.
We buy farm inputs with the combined power of our farmer network, and store it in 20 warehouses like this.
Imagine a professional social network that, instead of measuring its success in terms of connections created or messages sent, instead measured its success in terms of the job offers that people got that they were excited to get.
So what's going on between the pixels, between the image of the bird and the word "bird," is essentially a set of neurons connected to each other in a neural network, as I'm diagramming here.
This neural network could be biological, inside our visual cortices, or, nowadays, we start to have the capability to model such neural networks on the computer.
The behavior of this network is characterized by the strengths of all of those synapses.
Those characterize the computational properties of this network.
There are billions or trillions of w's, which represent the weights of all these synapses in the neural network.
And there's a very small number of y's, of outputs that that network has.
You know the neural network, you know the pixels.
I'll show you an artificial neural network that we've built recently, doing exactly that.
What you're looking at is a phone looking at one after another picture of a bird, and actually not only saying, "Yes, it's a bird," but identifying the species of bird with a network of this sort.
In other words, you know that it's a bird, and you already have your neural network that you've trained on birds, but what is the picture of a bird?
It turns out that by using exactly the same error-minimization procedure, one can do that with the network trained to recognize birds, and the result turns out to be ... a picture of birds.
So this is a picture of birds generated entirely by a neural network that was trained to recognize birds, just by solving for x rather than solving for y, and doing that iteratively.
In this case, what Mike is doing is varying y over the space of different animals, in a network designed to recognize and distinguish different animals from each other.
Here he and Alex together have tried reducing the y's to a space of only two dimensions, thereby making a map out of the space of all things recognized by this network.
This is a network designed to recognize faces, to distinguish one face from another.
The reason it looks like multiple points of view at once is because that network is designed to get rid of the ambiguity of a face being in one pose or another pose, being looked at with one kind of lighting, another kind of lighting.
Here we're starting with just a picture of clouds, and as we optimize, basically, this network is figuring out what it sees in the clouds.
You could also use the face network to hallucinate into this, and you get some pretty crazy stuff.
And in this way, you can get a sort of fugue state of the network, I suppose, or a sort of free association, in which the network is eating its own tail.
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