GOTO 2016 • Machine Learning with Google Cloud Platform • Kaz Sato

I’m Kaz Sato I’m a developer advocate

I’m Kaz Sato I’m a developer advocate for Google cloud platform and I’m also a

for Google cloud platform and I’m also a

for Google cloud platform and I’m also a tech lead for the data analytics team

tech lead for the data analytics team

tech lead for the data analytics team and I have been working at Google for

and I have been working at Google for

and I have been working at Google for over five years and for the last one and

over five years and for the last one and

over five years and for the last one and a half years I have been working as a

a half years I have been working as a

a half years I have been working as a developer advocate to having a

developer advocate to having a

developer advocate to having a presentation like this at many events so

presentation like this at many events so

presentation like this at many events so in these sessions I’d like to talk about

in these sessions I’d like to talk about

in these sessions I’d like to talk about these agendas but first I’d like to

these agendas but first I’d like to

these agendas but first I’d like to introduction introduce the concept of

introduction introduce the concept of

introduction introduce the concept of neural network and deep learning and how

neural network and deep learning and how

neural network and deep learning and how it works with very great demonstrations

it works with very great demonstrations

it works with very great demonstrations and also I’d like to introduce how

and also I’d like to introduce how

and also I’d like to introduce how Google has been deploying those neural

Google has been deploying those neural

Google has been deploying those neural network technologies to the Google crowd

network technologies to the Google crowd

network technologies to the Google crowd and then I’ll be covering the

and then I’ll be covering the

and then I’ll be covering the technologies and products that those are

technologies and products that those are

technologies and products that those are actually provided as a products from

actually provided as a products from

actually provided as a products from Google cloud platforms so what what is

Google cloud platforms so what what is

Google cloud platforms so what what is neural network so neural network is a

neural network so neural network is a

neural network so neural network is a function that can learn from training

function that can learn from training

function that can learn from training data set so if you want to have newer

data set so if you want to have newer

data set so if you want to have newer networks to do image recognition then

networks to do image recognition then

networks to do image recognition then you can put the for example cat image is

you can put the for example cat image is

you can put the for example cat image is converted into a load vector and then

converted into a load vector and then

converted into a load vector and then put that vector into the networks then

put that vector into the networks then

put that vector into the networks then eventually you’d have another output

eventually you’d have another output

eventually you’d have another output vector which represents the labels of

vector which represents the labels of

vector which represents the labels of the objects detect such as cat or human

the objects detect such as cat or human

the objects detect such as cat or human face so it’s designed to mimic the

face so it’s designed to mimic the

face so it’s designed to mimic the behavior of new ones inside human brains

behavior of new ones inside human brains

behavior of new ones inside human brains by using matrix operations so actually

by using matrix operations so actually

by using matrix operations so actually it’s really it has very basic matrix

it’s really it has very basic matrix

it’s really it has very basic matrix operations only there is no

operations only there is no

operations only there is no sophisticated or fancy mathematics going

sophisticated or fancy mathematics going

sophisticated or fancy mathematics going on everything you do with neural

on everything you do with neural

on everything you do with neural networks is you have if you matrix

networks is you have if you matrix

networks is you have if you matrix operations we have learned at high

operations we have learned at high

operations we have learned at high school for example the input vector may

school for example the input vector may

school for example the input vector may we present represent the image of cat

we present represent the image of cat

we present represent the image of cat then they will have a vector that would

then they will have a vector that would

then they will have a vector that would have the pixel data converting it to a

have the pixel data converting it to a

have the pixel data converting it to a vet

vet

vet and you would get another output vector

and you would get another output vector

and you would get another output vector as a result that represents the label of

as a result that represents the label of

as a result that represents the label of the detected images in in this case you

the detected images in in this case you

the detected images in in this case you will have the any number closer to the

will have the any number closer to the

will have the any number closer to the 1.0 that indicates the neural networks

1.0 that indicates the neural networks

1.0 that indicates the neural networks thinks the image must be a cat but you

thinks the image must be a cat but you

thinks the image must be a cat but you think’s going on inside in your networks

think’s going on inside in your networks

think’s going on inside in your networks is very simple all you are doing with

is very simple all you are doing with

is very simple all you are doing with neural network is the matrix operations

neural network is the matrix operations

neural network is the matrix operations like W X plus B equals wise where the WS

like W X plus B equals wise where the WS

like W X plus B equals wise where the WS are weights and B are biases and

are weights and B are biases and

are weights and B are biases and actually you don’t have to care about

actually you don’t have to care about

actually you don’t have to care about those WS and B’s at all you let

those WS and B’s at all you let

those WS and B’s at all you let computers find and calculate the WS and

computers find and calculate the WS and

computers find and calculate the WS and B’s so all you have to care is the yeah

B’s so all you have to care is the yeah

B’s so all you have to care is the yeah what kind of data you would want to put

what kind of data you would want to put

what kind of data you would want to put into the networks and what kind of the

into the networks and what kind of the

into the networks and what kind of the result you want to get so let’s take a

result you want to get so let’s take a

result you want to get so let’s take a look at the some interesting

look at the some interesting

look at the some interesting demonstration of neural networks for

demonstration of neural networks for

demonstration of neural networks for example if you have a problem like this

example if you have a problem like this

example if you have a problem like this the very simple classifications how you

the very simple classifications how you

the very simple classifications how you can use neural networks to do the

can use neural networks to do the

can use neural networks to do the classifications of these two different

classifications of these two different

classifications of these two different data sets I’m not sure what that those

data sets I’m not sure what that those

data sets I’m not sure what that those data point means but let’s take a lift

data point means but let’s take a lift

data point means but let’s take a lift imagine that those are the data of the

imagine that those are the data of the

imagine that those are the data of the weights and height of people so that if

weights and height of people so that if

weights and height of people so that if a person’s weight and height are both

a person’s weight and height are both

a person’s weight and height are both loud and you can think he or she must be

loud and you can think he or she must be

loud and you can think he or she must be a child or maybe he or she could be a

a child or maybe he or she could be a

a child or maybe he or she could be a adult how how can you classify them if

adult how how can you classify them if

adult how how can you classify them if you using you if you try to use neural

you using you if you try to use neural

you using you if you try to use neural networks to do this problem then you can

networks to do this problem then you can

networks to do this problem then you can just apply the same equation W X plus B

just apply the same equation W X plus B

just apply the same equation W X plus B equal Y to do you’ve to do two

equal Y to do you’ve to do two

equal Y to do you’ve to do two classifications so you are putting the

classifications so you are putting the

classifications so you are putting the weight number and height number here as

weight number and height number here as

weight number and height number here as a vector and you get an output a vector

a vector and you get an output a vector

a vector and you get an output a vector like this where if you if what single

like this where if you if what single

like this where if you if what single data points isn’t classified as an adult

data points isn’t classified as an adult

data points isn’t classified as an adult then you would have one here and if it’s

then you would have one here and if it’s

then you would have one here and if it’s a child then you would have a one here

a child then you would have a one here

a child then you would have a one here and thing is that computer tries to find

and thing is that computer tries to find

and thing is that computer tries to find the optimal combination of the

the optimal combination of the

the optimal combination of the parameters such as the weights and

parameters such as the weights and

parameters such as the weights and biases by itself and you don’t have to

biases by itself and you don’t have to

biases by itself and you don’t have to think about what kind of parameters you

think about what kind of parameters you

think about what kind of parameters you have to set to neural networks computer

have to set to neural networks computer

have to set to neural networks computer does it for you so let’s take a look at

does it for you so let’s take a look at

does it for you so let’s take a look at the actual demonstration did you see

the actual demonstration did you see

the actual demonstration did you see that so I can do that okay so instead of

that so I can do that okay so instead of

that so I can do that okay so instead of the humans letting computer instructing

the humans letting computer instructing

the humans letting computer instructing learning you know you don’t have to

learning you know you don’t have to

learning you know you don’t have to teach computers how to solve these

teach computers how to solve these

teach computers how to solve these problems but everything you have to do

problems but everything you have to do

problems but everything you have to do is provide the training data set so that

is provide the training data set so that

is provide the training data set so that computer thinks by itself to optimize

computer thinks by itself to optimize

computer thinks by itself to optimize the combination of the parameters such

the combination of the parameters such

the combination of the parameters such as weights and biases so you see that

as weights and biases so you see that

as weights and biases so you see that the computer is try to change the the

the computer is try to change the the

the computer is try to change the the weights to do the classifications at

weights to do the classifications at

weights to do the classifications at optimal success rate and now computer is

optimal success rate and now computer is

optimal success rate and now computer is using an algorithm is called gradient

using an algorithm is called gradient

using an algorithm is called gradient lee set that means it tries to increase

lee set that means it tries to increase

lee set that means it tries to increase or decrease each weights and biases to

or decrease each weights and biases to

or decrease each weights and biases to make the combinations closer through the

make the combinations closer through the

make the combinations closer through the higher accuracy or lower loss slate so

higher accuracy or lower loss slate so

higher accuracy or lower loss slate so it’s just like we’re learning things

it’s just like we’re learning things

it’s just like we’re learning things from the the parents or there may be a

from the the parents or there may be a

from the the parents or there may be a senior people in your company where you

senior people in your company where you

senior people in your company where you this junior people or these children are

this junior people or these children are

this junior people or these children are learning from many many mistakes so

learning from many many mistakes so

learning from many many mistakes so computer makes many many mistakes but in

computer makes many many mistakes but in

computer makes many many mistakes but in the initial stage but if you provide

the initial stage but if you provide

the initial stage but if you provide much much more training data set than

much much more training data set than

much much more training data set than computer using the gradient descent

computer using the gradient descent

computer using the gradient descent algorithms try to minimize the failures

algorithms try to minimize the failures

algorithms try to minimize the failures so that that’s how it works so let’s

so that that’s how it works so let’s

so that that’s how it works so let’s take a look at another interesting

take a look at another interesting

take a look at another interesting demonstrations of new or networks where

demonstrations of new or networks where

demonstrations of new or networks where you have the another training dataset

you have the another training dataset

you have the another training dataset like this I’m not sure what what does it

like this I’m not sure what what does it

like this I’m not sure what what does it mean but we think that we have a some

mean but we think that we have a some

mean but we think that we have a some data set which requires a complex

data set which requires a complex

data set which requires a complex classifications if you a program has

classifications if you a program has

classifications if you a program has maybe with this data setting you may

maybe with this data setting you may

maybe with this data setting you may want to user maybe equations for circle

want to user maybe equations for circle

want to user maybe equations for circle to classify the two datasets through

to classify the two datasets through

to classify the two datasets through datasets and arrange a disease but by

datasets and arrange a disease but by

datasets and arrange a disease but by using newer networks you can just let

using newer networks you can just let

using newer networks you can just let computer things how to solve it now you

computer things how to solve it now you

computer things how to solve it now you saw the computer was trying to create a

saw the computer was trying to create a

saw the computer was trying to create a pattern to classify those datasets by

pattern to classify those datasets by

pattern to classify those datasets by using so called neurons in the hidden

using so called neurons in the hidden

using so called neurons in the hidden layers with the first example we didn’t

layers with the first example we didn’t

layers with the first example we didn’t have any hidden layers but with this

have any hidden layers but with this

have any hidden layers but with this complex data that you would need to use

complex data that you would need to use

complex data that you would need to use the hidden layers that means between the

the hidden layers that means between the

the hidden layers that means between the input data and the output neurons you

input data and the output neurons you

input data and the output neurons you would have another layers between them

would have another layers between them

would have another layers between them there has much pool new ones and each

there has much pool new ones and each

there has much pool new ones and each new ones does very simple things these

new ones does very simple things these

new ones does very simple things these neurons only classifies whether the data

neurons only classifies whether the data

neurons only classifies whether the data points is in the in the bottom left red

points is in the in the bottom left red

points is in the in the bottom left red area or the upper right area or now

area or the upper right area or now

area or the upper right area or now these neurons only classifies whether

these neurons only classifies whether

these neurons only classifies whether the data points is in the left or right

the data points is in the left or right

the data points is in the left or right just like that but combining those

just like that but combining those

just like that but combining those outputs at the the last new one neural

outputs at the the last new one neural

outputs at the the last new one neural network then the neural networks can

network then the neural networks can

network then the neural networks can compose much more complex pattern like

compose much more complex pattern like

compose much more complex pattern like this and if you have more and more new

this and if you have more and more new

this and if you have more and more new neurons inside the hidden layers then

neurons inside the hidden layers then

neurons inside the hidden layers then the network specifications speculations

the network specifications speculations

the network specifications speculations can be much more accurate like this so

can be much more accurate like this so

can be much more accurate like this so here by adding more hidden layers with

here by adding more hidden layers with

here by adding more hidden layers with more new ones you have to spend much

more new ones you have to spend much

more new ones you have to spend much more computation power but at the same

more computation power but at the same

more computation power but at the same time the neural networks can compose

time the neural networks can compose

time the neural networks can compose much more complex patterns and extract

much more complex patterns and extract

much more complex patterns and extract the patterns from the large data set how

the patterns from the large data set how

the patterns from the large data set how about this let’s try it this is a data

about this let’s try it this is a data

about this let’s try it this is a data pattern called double spiral if you are

pattern called double spiral if you are

pattern called double spiral if you are a programmer and your director or cast

a programmer and your director or cast

a programmer and your director or cast asked you to cross fight this kind of

asked you to cross fight this kind of

asked you to cross fight this kind of data set what kind of your program

data set what kind of your program

data set what kind of your program called you drive do you want to write

called you drive do you want to write

called you drive do you want to write many issue statements or switch

many issue statements or switch

many issue statements or switch statements with many threshold try

statements with many threshold try

statements with many threshold try checking the x and y partitions no I

checking the x and y partitions no I

checking the x and y partitions no I don’t want to do that instead I would

don’t want to do that instead I would

don’t want to do that instead I would you be using neural networks so that

you be using neural networks so that

you be using neural networks so that neural networks try to looking at the

neural networks try to looking at the

neural networks try to looking at the data points in the data sets to find the

data points in the data sets to find the

data points in the data sets to find the optimal patterns hidden inside the

optimal patterns hidden inside the

optimal patterns hidden inside the training data set that’s right this so

training data set that’s right this so

training data set that’s right this so this is the where in your networks can

this is the where in your networks can

this is the where in your networks can exceed the human performance human

exceed the human performance human

exceed the human performance human programmers performance it can extract

programmers performance it can extract

programmers performance it can extract those hidden patterns inside the

those hidden patterns inside the

those hidden patterns inside the training data set and you don’t have to

training data set and you don’t have to

training data set and you don’t have to specify anything decorative features you

specify anything decorative features you

specify anything decorative features you don’t have to any actual features by

don’t have to any actual features by

don’t have to any actual features by human hand instead computers can find

human hand instead computers can find

human hand instead computers can find the patterns from the training data set

the patterns from the training data set

the patterns from the training data set so that why people are so excited with

so that why people are so excited with

so that why people are so excited with the neural networks and that deep

the neural networks and that deep

the neural networks and that deep learning about this so if you have the

learning about this so if you have the

learning about this so if you have the problems of the identifying handwriting

problems of the identifying handwriting

problems of the identifying handwriting text you can still using the very simple

text you can still using the very simple

text you can still using the very simple GW Express peep equals why kind of

GW Express peep equals why kind of

GW Express peep equals why kind of neural networks to cross find this

neural networks to cross find this

neural networks to cross find this handwriting takes the networks would

handwriting takes the networks would

handwriting takes the networks would come up with disease complex patterns

come up with disease complex patterns

come up with disease complex patterns stacking classify those images into the

stacking classify those images into the

stacking classify those images into the year vectors with the labels like an

year vectors with the labels like an

year vectors with the labels like an eight or seven or six if you want more

eight or seven or six if you want more

eight or seven or six if you want more accuracy than you would have more no

accuracy than you would have more no

accuracy than you would have more no more hidden dangers so that you you

more hidden dangers so that you you

more hidden dangers so that you you could get like a 85 or 95 or 98 like a

could get like a 85 or 95 or 98 like a

could get like a 85 or 95 or 98 like a machine how about this how can you

machine how about this how can you

machine how about this how can you classify these cat images by using

classify these cat images by using

classify these cat images by using neural networks you have to have many

neural networks you have to have many

neural networks you have to have many more layers of neural networks that is

more layers of neural networks that is

more layers of neural networks that is so-called deep neural networks

so-called deep neural networks

so-called deep neural networks this isn’t diagram is called inception

this isn’t diagram is called inception

this isn’t diagram is called inception model pattern 3 there has been published

model pattern 3 there has been published

model pattern 3 there has been published by Google last year where we have used

by Google last year where we have used

by Google last year where we have used 240 hidden layers in a single neural

240 hidden layers in a single neural

240 hidden layers in a single neural network design so it takes much more

network design so it takes much more

network design so it takes much more computation power and time but still we

computation power and time but still we

computation power and time but still we can

can

can much more complex competitions like this

much more complex competitions like this

much more complex competitions like this you know the new ones closer to the

you know the new ones closer to the

you know the new ones closer to the input vector could learn very simple

input vector could learn very simple

input vector could learn very simple pattern rugby’s like you know vertical

pattern rugby’s like you know vertical

pattern rugby’s like you know vertical lines or the horizontal rise but the

lines or the horizontal rise but the

lines or the horizontal rise but the neurons closer to the output vector

neurons closer to the output vector

neurons closer to the output vector could learn much more complex patterns

could learn much more complex patterns

could learn much more complex patterns or compositions such as eyes nose or

or compositions such as eyes nose or

or compositions such as eyes nose or human face again we didn’t put any

human face again we didn’t put any

human face again we didn’t put any features of patterns embedded in the

features of patterns embedded in the

features of patterns embedded in the neural networks before training did so

neural networks before training did so

neural networks before training did so everything can be trained from the data

everything can be trained from the data

everything can be trained from the data set

yeah by using computation power so

yeah by using computation power so that’s the how neural network and deep

that’s the how neural network and deep

that’s the how neural network and deep learning works but as I mentioned it

learning works but as I mentioned it

learning works but as I mentioned it takes so much computation time and

takes so much computation time and

takes so much computation time and training data sets to use the planning’s

training data sets to use the planning’s

training data sets to use the planning’s for the production projects so there are

for the production projects so there are

for the production projects so there are two big challenges right now for the

two big challenges right now for the

two big challenges right now for the users for the deep runnings and this is

users for the deep runnings and this is

users for the deep runnings and this is why keep running is has not been so

why keep running is has not been so

why keep running is has not been so popular for you guys once we have solved

popular for you guys once we have solved

popular for you guys once we have solved these problems raka will have a plenty

these problems raka will have a plenty

these problems raka will have a plenty of computation power with the printed

of computation power with the printed

of computation power with the printed training data set then you can easily

training data set then you can easily

training data set then you can easily apply neural network so deep learning to

apply neural network so deep learning to

apply neural network so deep learning to your existing problems if you a game

your existing problems if you a game

your existing problems if you a game programmer you may want to apply the

programmer you may want to apply the

programmer you may want to apply the deep learnings to analyzing gear you are

deep learnings to analyzing gear you are

deep learnings to analyzing gear you are your game log server logs to check

your game log server logs to check

your game log server logs to check whether a player could be an cheatin

whether a player could be an cheatin

whether a player could be an cheatin player or spammer office weather or if

player or spammer office weather or if

player or spammer office weather or if you web designer or web systems engineer

you web designer or web systems engineer

you web designer or web systems engineer for the ad system then you may want to

for the ad system then you may want to

for the ad system then you may want to apply the logs for the as conversion or

apply the logs for the as conversion or

apply the logs for the as conversion or quick-quick flow rate our logs to neural

quick-quick flow rate our logs to neural

quick-quick flow rate our logs to neural network so that you can get you can have

network so that you can get you can have

network so that you can get you can have computers to learn from the year as log

computers to learn from the year as log

computers to learn from the year as log to get more optimization but you have to

to get more optimization but you have to

to get more optimization but you have to have computation power and training data

have computation power and training data

have computation power and training data so that’s the reason why we have started

so that’s the reason why we have started

so that’s the reason why we have started using Google cloud to train in large

using Google cloud to train in large

using Google cloud to train in large scale neural network Google cloud has

scale neural network Google cloud has

scale neural network Google cloud has racket and

racket and

racket and hundreds of thousand machines in our

hundreds of thousand machines in our

hundreds of thousand machines in our data centers in global and we have been

data centers in global and we have been

data centers in global and we have been building those computers at the data

building those computers at the data

building those computers at the data center as a computer not just a bunch of

center as a computer not just a bunch of

center as a computer not just a bunch of the computers Switzer building we design

the computers Switzer building we design

the computers Switzer building we design each Craster which holds like a ten or

each Craster which holds like a ten or

each Craster which holds like a ten or twenty thousand servers working as a

twenty thousand servers working as a

twenty thousand servers working as a single computer with a multiple our CPUs

single computer with a multiple our CPUs

single computer with a multiple our CPUs so that’s the reason why we can it’s

so that’s the reason why we can it’s

so that’s the reason why we can it’s it’s not so hard for us to deploy try to

it’s not so hard for us to deploy try to

it’s not so hard for us to deploy try to scale neural networks or odds can be

scale neural networks or odds can be

scale neural networks or odds can be created Processing’s to our google cloud

created Processing’s to our google cloud

created Processing’s to our google cloud if there are two basic very fundamental

if there are two basic very fundamental

if there are two basic very fundamental technologies inside google’s that

technologies inside google’s that

technologies inside google’s that supports the data center the computer

supports the data center the computer

supports the data center the computer one is the network we have been building

one is the network we have been building

one is the network we have been building our own hardware support for the network

our own hardware support for the network

our own hardware support for the network switch fabric that is called jupiter

switch fabric that is called jupiter

switch fabric that is called jupiter networks so we union we are not using

networks so we union we are not using

networks so we union we are not using the commercial network switches for most

the commercial network switches for most

the commercial network switches for most cases such as the Cisco or juniper

cases such as the Cisco or juniper

cases such as the Cisco or juniper routers those are not mainstream of our

routers those are not mainstream of our

routers those are not mainstream of our new our network backbones we we have

new our network backbones we we have

new our network backbones we we have been building our own hardware that can

been building our own hardware that can

been building our own hardware that can hold like a hundred thousand pots of 10

hold like a hundred thousand pots of 10

hold like a hundred thousand pots of 10 Gigabit Ethernet ports that can eat at

Gigabit Ethernet ports that can eat at

Gigabit Ethernet ports that can eat at one point to pet a bit per second per

one point to pet a bit per second per

one point to pet a bit per second per our datacenter so that is the networks

our datacenter so that is the networks

our datacenter so that is the networks we have at Google and also contain a

we have at Google and also contain a

we have at Google and also contain a technology called Borg bo is our

technology called Borg bo is our

technology called Borg bo is our proprietary container technologies we

proprietary container technologies we

proprietary container technologies we have been using over 10 years for for

have been using over 10 years for for

have been using over 10 years for for deploying or almost all Google services

deploying or almost all Google services

deploying or almost all Google services such as Google search or Gmail or Google

such as Google search or Gmail or Google

such as Google search or Gmail or Google Maps

Maps

Maps bulk containers account hold up to

bulk containers account hold up to

bulk containers account hold up to 10,000 or 20,000 physical servers in a

10,000 or 20,000 physical servers in a

10,000 or 20,000 physical servers in a single cluster so that you can do the

single cluster so that you can do the

single cluster so that you can do the large scale job scheduling right the

large scale job scheduling right the

large scale job scheduling right the scheduling the CPU cycles or memory

scheduling the CPU cycles or memory

scheduling the CPU cycles or memory spaces or disk i/os with that scale so

spaces or disk i/os with that scale so

spaces or disk i/os with that scale so that reason why you can deploy your

that reason why you can deploy your

that reason why you can deploy your single applications like did the neural

single applications like did the neural

single applications like did the neural network training or the big data

network training or the big data

network training or the big data processing into maybe hundreds or

processing into maybe hundreds or

processing into maybe hundreds or thousands of machines with a single land

thousands of machines with a single land

thousands of machines with a single land of default command

of default command

of default command and Google brain is the project where we

and Google brain is the project where we

and Google brain is the project where we have started applying the Google cloud

have started applying the Google cloud

have started applying the Google cloud technology 2d to build a large-scale

technology 2d to build a large-scale

technology 2d to build a large-scale neural networks this project has started

neural networks this project has started

neural networks this project has started in 2011 and right now the Google brain

in 2011 and right now the Google brain

in 2011 and right now the Google brain has been used for the many many

has been used for the many many

has been used for the many many production project in Google and fast

production project in Google and fast

production project in Google and fast the scalability of Google Google brain

the scalability of Google Google brain

the scalability of Google Google brain project for example rankbrain rankbrain

project for example rankbrain rankbrain

project for example rankbrain rankbrain is our GU algorithms we are using for

is our GU algorithms we are using for

is our GU algorithms we are using for the ranking of Google search service

the ranking of Google search service

the ranking of Google search service right now since last year that has been

right now since last year that has been

right now since last year that has been using Google Google brain infrastructure

using Google Google brain infrastructure

using Google Google brain infrastructure and with five hundred nodes and that can

and with five hundred nodes and that can

and with five hundred nodes and that can perform at three hundred times faster

perform at three hundred times faster

perform at three hundred times faster than single node so that means if you

than single node so that means if you

than single node so that means if you are training your deep learning model

are training your deep learning model

are training your deep learning model with single servers then you would take

with single servers then you would take

with single servers then you would take 300 times longer than Google engineers

300 times longer than Google engineers

300 times longer than Google engineers and inception is the model for the

and inception is the model for the

and inception is the model for the visual visual recognition we can use 50

visual visual recognition we can use 50

visual visual recognition we can use 50 GPUs to accelerate the performance at 40

GPUs to accelerate the performance at 40

GPUs to accelerate the performance at 40 times faster so that reason those are

times faster so that reason those are

times faster so that reason those are the reason why Google has been so strong

the reason why Google has been so strong

the reason why Google has been so strong on applying deep running for the

on applying deep running for the

on applying deep running for the production project such as the alphago

production project such as the alphago

production project such as the alphago Frady we have the series of the core

Frady we have the series of the core

Frady we have the series of the core matches with the core professional they

matches with the core professional they

matches with the core professional they have been using the Google brain

have been using the Google brain

have been using the Google brain infrastructure for the training as well

infrastructure for the training as well

infrastructure for the training as well as the prediction of the Google match

as the prediction of the Google match

as the prediction of the Google match Google search has been using deep with

Google search has been using deep with

Google search has been using deep with the brain of since last year and we have

the brain of since last year and we have

the brain of since last year and we have been using the machine learning

been using the machine learning

been using the machine learning technologies for the optimizing the data

technologies for the optimizing the data

technologies for the optimizing the data center operation and also or she our

center operation and also or she our

center operation and also or she our natural language processing and visual

natural language processing and visual

natural language processing and visual recognition of speech recognition such

recognition of speech recognition such

recognition of speech recognition such as the Google photos what he voiced the

as the Google photos what he voiced the

as the Google photos what he voiced the conventions of the androids we have over

conventions of the androids we have over

conventions of the androids we have over 60 production projects that has been

60 production projects that has been

60 production projects that has been using Google brain and deep learnings

using Google brain and deep learnings

using Google brain and deep learnings for last a couple of years now we have

for last a couple of years now we have

for last a couple of years now we have started to externalizing this power of

started to externalizing this power of

started to externalizing this power of Google brain to external developers

the first product is called crowd vision

the first product is called crowd vision API and the second product is called

API and the second product is called

API and the second product is called crowd speech API crowd vision API is an

crowd speech API crowd vision API is an

crowd speech API crowd vision API is an image analysis IPA that provides the

image analysis IPA that provides the

image analysis IPA that provides the pre-trained model so you don’t have to

pre-trained model so you don’t have to

pre-trained model so you don’t have to train your own neural network and you

train your own neural network and you

train your own neural network and you also don’t have to have the any skill

also don’t have to have the any skill

also don’t have to have the any skill set for the machine learning so it’s

set for the machine learning so it’s

set for the machine learning so it’s just on REST API you can just upload

just on REST API you can just upload

just on REST API you can just upload your photo image to API then you repeat

your photo image to API then you repeat

your photo image to API then you repeat receiving JSON result in a few seconds

receiving JSON result in a few seconds

receiving JSON result in a few seconds there has the the analysis result and

there has the the analysis result and

there has the the analysis result and it’s free to start trying out up to

it’s free to start trying out up to

it’s free to start trying out up to 1,000 images per month and it’s general

1,000 images per month and it’s general

1,000 images per month and it’s general generally available right now so it’s

generally available right now so it’s

generally available right now so it’s ready to be used for the production

ready to be used for the production

ready to be used for the production project it has six different features to

project it has six different features to

project it has six different features to be detected labial detections means that

be detected labial detections means that

be detected labial detections means that you can put any labels or categories on

you can put any labels or categories on

you can put any labels or categories on any images you uploaded for example if

any images you uploaded for example if

any images you uploaded for example if you uploading the cat images then the

you uploading the cat images then the

you uploading the cat images then the API will be returning the Arabians such

API will be returning the Arabians such

API will be returning the Arabians such as a cat or pet face detections can

as a cat or pet face detections can

as a cat or pet face detections can detect the location of face in the image

detect the location of face in the image

detect the location of face in the image OCR I can convert the text on image to a

OCR I can convert the text on image to a

OCR I can convert the text on image to a string explicit content detection means

string explicit content detection means

string explicit content detection means that you can check whether the images

that you can check whether the images

that you can check whether the images can contain the images contain the the

can contain the images contain the the

can contain the images contain the the adult or violent images on our landmark

adult or violent images on our landmark

adult or violent images on our landmark detection can detect the location of the

detection can detect the location of the

detection can detect the location of the images or popular places and you can

images or popular places and you can

images or popular places and you can also detect the product or corporate

also detect the product or corporate

also detect the product or corporate role let’s take a look at the

role let’s take a look at the

role let’s take a look at the demonstration

so I’d like to show a demonstration by

so I’d like to show a demonstration by video at first this is the

video at first this is the

video at first this is the demonstrations by using the Raspberry Pi

demonstrations by using the Raspberry Pi

demonstrations by using the Raspberry Pi robot that sends the image to division

robot that sends the image to division

robot that sends the image to division API cloud vision provides powerful image

API cloud vision provides powerful image

API cloud vision provides powerful image analytics capabilities as easy to use

analytics capabilities as easy to use

analytics capabilities as easy to use api’s it enables application developers

api’s it enables application developers

api’s it enables application developers to build the next generation of

to build the next generation of

to build the next generation of application that can see and understand

application that can see and understand

application that can see and understand the content within the images the

the content within the images the

the content within the images the service is built on powerful computer

service is built on powerful computer

service is built on powerful computer vision models that power several to firm

vision models that power several to firm

vision models that power several to firm Google services the service enables

Google services the service enables

Google services the service enables developers to detect a broad set of

developers to detect a broad set of

developers to detect a broad set of entities within an image from everyday

entities within an image from everyday

entities within an image from everyday objects to faces in product logos the

objects to faces in product logos the

objects to faces in product logos the service is so easy to use as one example

service is so easy to use as one example

service is so easy to use as one example of the use cases you can have any

of the use cases you can have any

of the use cases you can have any Raspberry Pi robot like gulp I go

Raspberry Pi robot like gulp I go

Raspberry Pi robot like gulp I go calling the cloud vision API directly so

calling the cloud vision API directly so

calling the cloud vision API directly so the broad can sum the images taken by

the broad can sum the images taken by

the broad can sum the images taken by its camera to the cloud and can get the

its camera to the cloud and can get the

its camera to the cloud and can get the analysis results in real time it detects

analysis results in real time it detects

analysis results in real time it detects faces in the image along with the

faces in the image along with the

faces in the image along with the associated emotions the cloud vision API

associated emotions the cloud vision API

associated emotions the cloud vision API is also able to detect entities within

is also able to detect entities within

is also able to detect entities within the image now let’s see how facial

the image now let’s see how facial

the image now let’s see how facial detection works cloud vision detect

detection works cloud vision detect

detection works cloud vision detect spaces on the picture and returns the

spaces on the picture and returns the

spaces on the picture and returns the positions of eyes nose and mouth so you

positions of eyes nose and mouth so you

positions of eyes nose and mouth so you can program the bot to follow the face

it also detects emotions such as joy

it also detects emotions such as joy anger surprise and sorrow so the bottom

anger surprise and sorrow so the bottom

anger surprise and sorrow so the bottom moved toward smiling faces or avoid

moved toward smiling faces or avoid

moved toward smiling faces or avoid anger or surprise face one of the very

anger or surprise face one of the very

anger or surprise face one of the very interesting features of cloud vision API

interesting features of cloud vision API

interesting features of cloud vision API is the entity detection that means it

is the entity detection that means it

is the entity detection that means it detects any objects you like you see

detects any objects you like you see

detects any objects you like you see cloud visitors likes developers to take

cloud visitors likes developers to take

cloud visitors likes developers to take advantage of Google’s latest machine

advantage of Google’s latest machine

advantage of Google’s latest machine learning technologies quite easily

learning technologies quite easily

learning technologies quite easily please go to cloud.google.com slash

please go to cloud.google.com slash

please go to cloud.google.com slash vision to learn more and I have another

vision to learn more and I have another

vision to learn more and I have another interesting demonstrations that is made

interesting demonstrations that is made

interesting demonstrations that is made by using the vision API that if this is

by using the vision API that if this is

by using the vision API that if this is called vision Explorer demonstrations

called vision Explorer demonstrations

called vision Explorer demonstrations where we have imported 80,000 images

where we have imported 80,000 images

where we have imported 80,000 images downloaded from Wikimedia Commons and

downloaded from Wikimedia Commons and

downloaded from Wikimedia Commons and uploaded to the Google Cloud storage and

uploaded to the Google Cloud storage and

uploaded to the Google Cloud storage and applied the vision API analysis so here

applied the vision API analysis so here

applied the vision API analysis so here we have the cluster of the images it is

we have the cluster of the images it is

we have the cluster of the images it is 80 thousand images and each cluster has

80 thousand images and each cluster has

80 thousand images and each cluster has the labels such as snow or transport

the labels such as snow or transport

the labels such as snow or transport residential area means that the the

residential area means that the the

residential area means that the the cluster of the similar images for

cluster of the similar images for

cluster of the similar images for example if you take a look at here let’s

example if you take a look at here let’s

example if you take a look at here let’s go to a plant so each single dot

go to a plant so each single dot

go to a plant so each single dot represents your thumbnail of the

represents your thumbnail of the

represents your thumbnail of the uploaded images so if you go to the

uploaded images so if you go to the

uploaded images so if you go to the plant

plant

plant Craster there must be some cluster of

Craster there must be some cluster of

Craster there must be some cluster of the oh it’s oh it’s not showing why let

the oh it’s oh it’s not showing why let

the oh it’s oh it’s not showing why let me redraw this maybe because I’m using

me redraw this maybe because I’m using

me redraw this maybe because I’m using tethering

okay let’s go directly to the cat

okay let’s go directly to the cat cluster so in this cluster we have many

cluster so in this cluster we have many

cluster so in this cluster we have many many cats and closer to the cat cora’s

many cats and closer to the cat cora’s

many cats and closer to the cat cora’s we have the crust for dogs let’s go back

we have the crust for dogs let’s go back

we have the crust for dogs let’s go back to the cat cluster and if you click to

to the cat cluster and if you click to

to the cat cluster and if you click to image thumbnail image and you’ll be

image thumbnail image and you’ll be

image thumbnail image and you’ll be seeing the analysis result from the API

seeing the analysis result from the API

seeing the analysis result from the API right this the API thinks this must be a

right this the API thinks this must be a

right this the API thinks this must be a mimic of cat and it’s a cat as a pet or

mimic of cat and it’s a cat as a pet or

mimic of cat and it’s a cat as a pet or it must it must be a British Shorthair

it must it must be a British Shorthair

it must it must be a British Shorthair so this is these some things you can do

so this is these some things you can do

so this is these some things you can do with the deep learning technology and

with the deep learning technology and

with the deep learning technology and those results are returned in a JSON

those results are returned in a JSON

those results are returned in a JSON format erectus and with statistical

format erectus and with statistical

format erectus and with statistical stretches we can show it in a GUI if

stretches we can show it in a GUI if

stretches we can show it in a GUI if image contains any text inside it then

image contains any text inside it then

image contains any text inside it then we can come back convert it into the

we can come back convert it into the

we can come back convert it into the extreme example with this you can have

extreme example with this you can have

extreme example with this you can have the string like these three Kangaroos

the string like these three Kangaroos

the string like these three Kangaroos crossing next to you images if images

crossing next to you images if images

crossing next to you images if images contains faces also this API doesn’t

contains faces also this API doesn’t

contains faces also this API doesn’t support any personal identification or

support any personal identification or

support any personal identification or the personal recognition but it can

the personal recognition but it can

the personal recognition but it can detect the location of the faces with

detect the location of the faces with

detect the location of the faces with landmark locations such as nose and

landmark locations such as nose and

landmark locations such as nose and mouth and also it can recognize the

mouth and also it can recognize the

mouth and also it can recognize the emotions such as joy sorrow and anger

emotions such as joy sorrow and anger

emotions such as joy sorrow and anger and surprising in this confidence level

and surprising in this confidence level

and surprising in this confidence level and if your picture contains any popular

and if your picture contains any popular

and if your picture contains any popular places such as this dinner API can

places such as this dinner API can

places such as this dinner API can return the name of the landmark the API

return the name of the landmark the API

return the name of the landmark the API thinks it must be an image of the Citi

thinks it must be an image of the Citi

thinks it must be an image of the Citi Field Stadium in New York City with the

Field Stadium in New York City with the

Field Stadium in New York City with the longitude and latitude so you can easily

longitude and latitude so you can easily

longitude and latitude so you can easily in a put a marker on the Google Maps

in a put a marker on the Google Maps

in a put a marker on the Google Maps it’s too slow so I’m cutting it off also

it’s too slow so I’m cutting it off also

it’s too slow so I’m cutting it off also you can detect the product and corporate

you can detect the product and corporate

you can detect the product and corporate robot

like this Android so this was the this

like this Android so this was the this vision API so it’s ready to be used for

vision API so it’s ready to be used for

vision API so it’s ready to be used for any applications and another API is

any applications and another API is

any applications and another API is called speech API which also provides

called speech API which also provides

called speech API which also provides the pre-trained model for the voice

the pre-trained model for the voice

the pre-trained model for the voice recognition so you don’t have to have

recognition so you don’t have to have

recognition so you don’t have to have any skill set or experiments with the

any skill set or experiments with the

any skill set or experiments with the voice recognition or training neural

voice recognition or training neural

voice recognition or training neural networks for doing doing that it’s just

networks for doing doing that it’s just

networks for doing doing that it’s just on REST API and G RPC API so you can

on REST API and G RPC API so you can

on REST API and G RPC API so you can just upload your audio data to the API

just upload your audio data to the API

just upload your audio data to the API and you’ll be receiving a result in the

and you’ll be receiving a result in the

and you’ll be receiving a result in the few seconds it supports over 80

few seconds it supports over 80

few seconds it supports over 80 languages and dialects it supports both

languages and dialects it supports both

languages and dialects it supports both real-time recognition and battery

real-time recognition and battery

real-time recognition and battery recognition the API is still in limited

recognition the API is still in limited

recognition the API is still in limited preview so if you go to the speech

preview so if you go to the speech

preview so if you go to the speech cloud.google.com speech then you have to

cloud.google.com speech then you have to

cloud.google.com speech then you have to sign up with the form for immediate

sign up with the form for immediate

sign up with the form for immediate limited preview access but we hope to

limited preview access but we hope to

limited preview access but we hope to make it public better maybe in a couple

make it public better maybe in a couple

make it public better maybe in a couple of weeks I suppose let’s show some

of weeks I suppose let’s show some

of weeks I suppose let’s show some demonstration I’m not sure if this works

demonstration I’m not sure if this works

demonstration I’m not sure if this works in the event or not because this is the

in the event or not because this is the

in the event or not because this is the first time to try this and and I have

first time to try this and and I have

first time to try this and and I have some accent problems so I’m not sure I

some accent problems so I’m not sure I

some accent problems so I’m not sure I really not sure if this works or not but

really not sure if this works or not but

really not sure if this works or not but best right hello this is a testing of I

best right hello this is a testing of I

best right hello this is a testing of I think it’s not working

hello this is a test of voice

hello this is a test of voice recognition bar by Google Cloud machine

recognition bar by Google Cloud machine

recognition bar by Google Cloud machine learning oh yeah

learning oh yeah

learning oh yeah so final result is you know not bad

so final result is you know not bad

so final result is you know not bad right and you also saw the fast response

right and you also saw the fast response

right and you also saw the fast response so you could get the recognition result

so you could get the recognition result

so you could get the recognition result in recent one second district Iraq a 0.5

in recent one second district Iraq a 0.5

in recent one second district Iraq a 0.5 seconds in real time so those are the

seconds in real time so those are the

seconds in real time so those are the api’s and but those api’s here are

api’s and but those api’s here are

api’s and but those api’s here are pre-trained model so that means you

pre-trained model so that means you

pre-trained model so that means you cannot train your own model with those

cannot train your own model with those

cannot train your own model with those aps and well one would if we country

aps and well one would if we country

aps and well one would if we country asked questions for those api is that

asked questions for those api is that

asked questions for those api is that whether the google will be you know

whether the google will be you know

whether the google will be you know looking at the uploaded images or the

looking at the uploaded images or the

looking at the uploaded images or the audio data to train your own model or

audio data to train your own model or

audio data to train your own model or doing some more research and as know

doing some more research and as know

doing some more research and as know those api saudi all of our products are

those api saudi all of our products are

those api saudi all of our products are provided by the Google cloud platform is

provided by the Google cloud platform is

provided by the Google cloud platform is is under the terms and conditions of DCP

is under the terms and conditions of DCP

is under the terms and conditions of DCP that has here some sections for the

that has here some sections for the

that has here some sections for the customer customer data we don’t look at

customer customer data we don’t look at

customer customer data we don’t look at the customer data except for the very

the customer data except for the very

the customer data except for the very special cases for the troubleshooting or

special cases for the troubleshooting or

special cases for the troubleshooting or emergency situation so basically we

emergency situation so basically we

emergency situation so basically we don’t look at the gyro data uploaded to

don’t look at the gyro data uploaded to

don’t look at the gyro data uploaded to the cloud but at the same time so you

the cloud but at the same time so you

the cloud but at the same time so you can so the APS cannot train cannot do

can so the APS cannot train cannot do

can so the APS cannot train cannot do the trainings for your data or your

the trainings for your data or your

the trainings for your data or your applications so that’s the reason why we

applications so that’s the reason why we

applications so that’s the reason why we provide the other options for machine

provide the other options for machine

provide the other options for machine learning with a stencil or cloud machine

learning with a stencil or cloud machine

learning with a stencil or cloud machine learning the other d-dick frameworks and

learning the other d-dick frameworks and

learning the other d-dick frameworks and platforms that can used for train your

platforms that can used for train your

platforms that can used for train your own data set train your own machine

own data set train your own machine

own data set train your own machine learning and neural network what is 10

learning and neural network what is 10

learning and neural network what is 10 so for tensile Pro is an open-source

so for tensile Pro is an open-source

so for tensile Pro is an open-source driver of your machine intelligence we

driver of your machine intelligence we

driver of your machine intelligence we have published the libraries last

have published the libraries last

have published the libraries last November and this is the the

November and this is the the

November and this is the the actual framework we are right now using

actual framework we are right now using

actual framework we are right now using us via Google research of Google brain

us via Google research of Google brain

us via Google research of Google brain team so it’s not something’s

team so it’s not something’s

team so it’s not something’s outdated or stay out since the latest

outdated or stay out since the latest

outdated or stay out since the latest machine learning framework we are using

machine learning framework we are using

machine learning framework we are using are right now at Google for example if

are right now at Google for example if

are right now at Google for example if you want to design this

you want to design this

you want to design this restaurant work swag DW Express Pico why

restaurant work swag DW Express Pico why

restaurant work swag DW Express Pico why you can use Python to write it in a

you can use Python to write it in a

you can use Python to write it in a single line of code Roxas you can put

single line of code Roxas you can put

single line of code Roxas you can put the image of cat here this vector and

the image of cat here this vector and

the image of cat here this vector and then you would have an output vector

then you would have an output vector

then you would have an output vector that represents the labels of the

that represents the labels of the

that represents the labels of the detected objects like a cat or human

detected objects like a cat or human

detected objects like a cat or human face and you can let computers to find

face and you can let computers to find

face and you can let computers to find the obits and biases so it’s so simple

the obits and biases so it’s so simple

the obits and biases so it’s so simple and also it’s really simple to train

and also it’s really simple to train

and also it’s really simple to train your networks because you can just write

your networks because you can just write

your networks because you can just write this single line to have your networks

this single line to have your networks

this single line to have your networks trained for your training data set by

trained for your training data set by

trained for your training data set by using by specifying the algorithm

using by specifying the algorithm

using by specifying the algorithm Stryker gradient is set you don’t have

Stryker gradient is set you don’t have

Stryker gradient is set you don’t have to implement your own the procedural

to implement your own the procedural

to implement your own the procedural code or called – implementing the each

code or called – implementing the each

code or called – implementing the each the optimization logic actually I’m not

the optimization logic actually I’m not

the optimization logic actually I’m not good at math or those machine learning

good at math or those machine learning

good at math or those machine learning algorithms but still I can just copy and

algorithms but still I can just copy and

algorithms but still I can just copy and paste the sample code to my laptop and

paste the sample code to my laptop and

paste the sample code to my laptop and I’m praying with my own data sets missed

I’m praying with my own data sets missed

I’m praying with my own data sets missed and so forth so you can just let the

and so forth so you can just let the

and so forth so you can just let the chancel for runtimes to do the

chancel for runtimes to do the

chancel for runtimes to do the optimization and also the tool provides

optimization and also the tool provides

optimization and also the tool provides you a very good visualization tool so

you a very good visualization tool so

you a very good visualization tool so one of the problems we had at Google for

one of the problems we had at Google for

one of the problems we had at Google for applying the neural networks to the

applying the neural networks to the

applying the neural networks to the production production problem is the

production production problem is the

production production problem is the debugging so if you have many more

debugging so if you have many more

debugging so if you have many more hidden layers inside the neural networks

hidden layers inside the neural networks

hidden layers inside the neural networks you have to check the all these stages

you have to check the all these stages

you have to check the all these stages of the parameters whether the parameters

of the parameters whether the parameters

of the parameters whether the parameters are converging in a right direction or

are converging in a right direction or

are converging in a right direction or the parameters could be you know going

the parameters could be you know going

the parameters could be you know going away and having a wrong number such as a

away and having a wrong number such as a

away and having a wrong number such as a na or 0 elsewhere so it’s really

na or 0 elsewhere so it’s really

na or 0 elsewhere so it’s really important to visualize what’s happening

important to visualize what’s happening

important to visualize what’s happening inside in your networks and tensorflow

inside in your networks and tensorflow

inside in your networks and tensorflow provides the tool and also the

provides the tool and also the

provides the tool and also the portability is another important aspect

portability is another important aspect

portability is another important aspect of the framework so once you have

of the framework so once you have

of the framework so once you have defined your neural networks with

defined your neural networks with

defined your neural networks with Python code of tensorflow then you can

Python code of tensorflow then you can

Python code of tensorflow then you can start running you and you ready to work

start running you and you ready to work

start running you and you ready to work training or prediction with your laptop

training or prediction with your laptop

training or prediction with your laptop like a Mac or Windows Raptor but you

like a Mac or Windows Raptor but you

like a Mac or Windows Raptor but you will find that your laptop is too slow

will find that your laptop is too slow

will find that your laptop is too slow to trying the trendy deep neural

to trying the trendy deep neural

to trying the trendy deep neural networks so maybe you may soon want to

networks so maybe you may soon want to

networks so maybe you may soon want to buy some GPU class and instead of maybe

buy some GPU class and instead of maybe

buy some GPU class and instead of maybe 2 or 3 GB because in a single box but

2 or 3 GB because in a single box but

2 or 3 GB because in a single box but still usually it takes like a few few

still usually it takes like a few few

still usually it takes like a few few days usually a few days or maybe some

days usually a few days or maybe some

days usually a few days or maybe some people spending a few weeks to to do the

people spending a few weeks to to do the

people spending a few weeks to to do the trainings on their neural networks so it

trainings on their neural networks so it

trainings on their neural networks so it takes so much computation time so in

takes so much computation time so in

takes so much computation time so in that case you can applaud your tensor

that case you can applaud your tensor

that case you can applaud your tensor flow graph to Google cloud so that you

flow graph to Google cloud so that you

flow graph to Google cloud so that you can utilize the power of the tents or

can utilize the power of the tents or

can utilize the power of the tents or maybe hundreds of GPU instances we have

maybe hundreds of GPU instances we have

maybe hundreds of GPU instances we have we’re running at Google cloud and also

we’re running at Google cloud and also

we’re running at Google cloud and also once you have finished your training

once you have finished your training

once you have finished your training then the size of the parameter sets

then the size of the parameter sets

then the size of the parameter sets could be fit into our hundreds of

could be fit into our hundreds of

could be fit into our hundreds of megabytes or tens of megabytes then you

megabytes or tens of megabytes then you

megabytes or tens of megabytes then you can easily copy that parameter sets into

can easily copy that parameter sets into

can easily copy that parameter sets into the smaller devices mobile devices or

the smaller devices mobile devices or

the smaller devices mobile devices or IOT devices such as Android iOS or maybe

IOT devices such as Android iOS or maybe

IOT devices such as Android iOS or maybe Raspberry Pi so that you know you can

Raspberry Pi so that you know you can

Raspberry Pi so that you know you can have those devices doing the prediction

have those devices doing the prediction

have those devices doing the prediction like image recognition or voice

like image recognition or voice

like image recognition or voice retention without using any internet

retention without using any internet

retention without using any internet connection

connection

connection everything could be implemented within

everything could be implemented within

everything could be implemented within the framework of tensorflow and with the

the framework of tensorflow and with the

the framework of tensorflow and with the at the last Google i/o it was about 1

at the last Google i/o it was about 1

at the last Google i/o it was about 1 months ago we have announced a new

months ago we have announced a new

months ago we have announced a new technology called tensor processing unit

technology called tensor processing unit

technology called tensor processing unit this is a replacement not a replacement

this is a replacement not a replacement

this is a replacement not a replacement maybe a complementary technology for the

maybe a complementary technology for the

maybe a complementary technology for the GPU and CPU so so far or maybe right now

GPU and CPU so so far or maybe right now

GPU and CPU so so far or maybe right now the any deep neural networks researchers

the any deep neural networks researchers

the any deep neural networks researchers or developers outside Google is using

or developers outside Google is using

or developers outside Google is using GPUs mostly for training the neural

GPUs mostly for training the neural

GPUs mostly for training the neural networks because it’s a matrix

networks because it’s a matrix

networks because it’s a matrix operations and by using GPUs you can

operations and by using GPUs you can

operations and by using GPUs you can accelerate the matrix office

accelerate the matrix office

accelerate the matrix office ten times or maybe 40 times faster so

ten times or maybe 40 times faster so

ten times or maybe 40 times faster so that’s what typical neural networks

that’s what typical neural networks

that’s what typical neural networks users are doing right now but the

users are doing right now but the

users are doing right now but the devices problem for GPU is the power

devices problem for GPU is the power

devices problem for GPU is the power consumption each consumers record 100

consumption each consumers record 100

consumption each consumers record 100 watts or 200 watts per GPU card and we

watts or 200 watts per GPU card and we

watts or 200 watts per GPU card and we are having we were using thousands of

are having we were using thousands of

are having we were using thousands of them in a Google Data Center and power

them in a Google Data Center and power

them in a Google Data Center and power Concepcion is becoming the Rogers

Concepcion is becoming the Rogers

Concepcion is becoming the Rogers problem so by by designing the Asics or

problem so by by designing the Asics or

problem so by by designing the Asics or the editorship specifically for the

the editorship specifically for the

the editorship specifically for the tensor flow or deep neural networks we

tensor flow or deep neural networks we

tensor flow or deep neural networks we were able to reduce the power

were able to reduce the power

were able to reduce the power consumption and gain the ten times

consumption and gain the ten times

consumption and gain the ten times better for our performance – powerful

better for our performance – powerful

better for our performance – powerful performance for what result and we also

performance for what result and we also

performance for what result and we also use the special techniques such as the

use the special techniques such as the

use the special techniques such as the bit quantization rather than using a

bit quantization rather than using a

bit quantization rather than using a 32-bit or 16-bit to calculate everything

32-bit or 16-bit to calculate everything

32-bit or 16-bit to calculate everything every matrix operations we use the

every matrix operations we use the

every matrix operations we use the quantization strike a quantized into the

quantization strike a quantized into the

quantization strike a quantized into the 8-bit where there’s not so not so much

8-bit where there’s not so not so much

8-bit where there’s not so not so much loss of the accuracy so that you can fit

loss of the accuracy so that you can fit

loss of the accuracy so that you can fit much bigger parameters into a very small

much bigger parameters into a very small

much bigger parameters into a very small memory footprint and we have been using

memory footprint and we have been using

memory footprint and we have been using GTP’s for many production projects

GTP’s for many production projects

GTP’s for many production projects already run greying alphago and google

already run greying alphago and google

already run greying alphago and google photos speech recognitions these are all

photos speech recognitions these are all

photos speech recognitions these are all has been using tepees since a couple

has been using tepees since a couple

has been using tepees since a couple months actually we have been using GPS

months actually we have been using GPS

months actually we have been using GPS for less than one year and the if you

for less than one year and the if you

for less than one year and the if you want to yeah we have been I haven’t

want to yeah we have been I haven’t

want to yeah we have been I haven’t discussed describing about the power of

discussed describing about the power of

discussed describing about the power of the Google brain such as number of the

the Google brain such as number of the

the Google brain such as number of the CPUs GPUs and TP is and if you want to

CPUs GPUs and TP is and if you want to

CPUs GPUs and TP is and if you want to utilize the power of Google brand

utilize the power of Google brand

utilize the power of Google brand infrastructure here’s the product we

infrastructure here’s the product we

infrastructure here’s the product we provide which is called cloud machine

provide which is called cloud machine

provide which is called cloud machine learning

learning

learning crud machine learning is a fully managed

crud machine learning is a fully managed

crud machine learning is a fully managed distributed training environment for

distributed training environment for

distributed training environment for your tester for graph so once you have

your tester for graph so once you have

your tester for graph so once you have written

written

written wrote your tensor flow graph and run run

wrote your tensor flow graph and run run

wrote your tensor flow graph and run run it on the laptop then you can upload the

it on the laptop then you can upload the

it on the laptop then you can upload the same types of rock for graph to Google

same types of rock for graph to Google

same types of rock for graph to Google Cloud messing learning so that you can

Cloud messing learning so that you can

Cloud messing learning so that you can specify the number of the GPS you want

specify the number of the GPS you want

specify the number of the GPS you want to use with the service suggested you

to use with the service suggested you

to use with the service suggested you such as 20 nodes or 50 notes to do the

such as 20 nodes or 50 notes to do the

such as 20 nodes or 50 notes to do the acceleration only training when a crowd

acceleration only training when a crowd

acceleration only training when a crowd Mao is in the limited preview so you

Mao is in the limited preview so you

Mao is in the limited preview so you have to sign up to start trying out but

have to sign up to start trying out but

have to sign up to start trying out but maybe I we suppose that the for

maybe I we suppose that the for

maybe I we suppose that the for availability record public better will

availability record public better will

availability record public better will be sometimes later in this year if you

be sometimes later in this year if you

be sometimes later in this year if you go to the YouTube then you can take a

go to the YouTube then you can take a

go to the YouTube then you can take a look at the actual demonstration of

look at the actual demonstration of

look at the actual demonstration of kratom a budget of teens where they have

kratom a budget of teens where they have

kratom a budget of teens where they have where he has presented demonstrated the

where he has presented demonstrated the

where he has presented demonstrated the actual chance of grotessa fraud based

actual chance of grotessa fraud based

actual chance of grotessa fraud based neural networks that takes 8 hours with

neural networks that takes 8 hours with

neural networks that takes 8 hours with single node but if you upload the same

single node but if you upload the same

single node but if you upload the same tensor flow graph to the crowd email

tensor flow graph to the crowd email

tensor flow graph to the crowd email then you can accelerate the performance

then you can accelerate the performance

then you can accelerate the performance to up to 15 times faster that means you

to up to 15 times faster that means you

to up to 15 times faster that means you could get the result of the trainings

could get the result of the trainings

could get the result of the trainings within 30 minutes rather than waiting

within 30 minutes rather than waiting

within 30 minutes rather than waiting for the 8 hours that is the speed we are

for the 8 hours that is the speed we are

for the 8 hours that is the speed we are seeing inside Google for any deep

seeing inside Google for any deep

seeing inside Google for any deep learning deployment and we externalizing

learning deployment and we externalizing

learning deployment and we externalizing the dispo to to you guys to have you

the dispo to to you guys to have you

the dispo to to you guys to have you utilizing the power for 40 you are for

utilizing the power for 40 you are for

utilizing the power for 40 you are for solving your own problems and also

solving your own problems and also

solving your own problems and also claudemir can be used for the production

claudemir can be used for the production

claudemir can be used for the production as well not only for the training and

as well not only for the training and

as well not only for the training and indeed mo solutions he presented at he

indeed mo solutions he presented at he

indeed mo solutions he presented at he demonstrated that the crowd email could

demonstrated that the crowd email could

demonstrated that the crowd email could be used for the body predictions at 300

be used for the body predictions at 300

be used for the body predictions at 300 meters per second so those are the the

meters per second so those are the the

meters per second so those are the the topics I have covered and now we have

topics I have covered and now we have

topics I have covered and now we have two different products one is the Amero

two different products one is the Amero

two different products one is the Amero api’s like a vision API or speech API

api’s like a vision API or speech API

api’s like a vision API or speech API where you can just upload your own data

where you can just upload your own data

where you can just upload your own data to cloud so that you will be getting the

to cloud so that you will be getting the

to cloud so that you will be getting the results in a few seconds and if you want

results in a few seconds and if you want

results in a few seconds and if you want to train your own neural networks

to train your own neural networks

to train your own neural networks then you can use a try using the

then you can use a try using the

then you can use a try using the intensive roll or crud machine learning

intensive roll or crud machine learning

intensive roll or crud machine learning so that you can accelerate training

so that you can accelerate training

so that you can accelerate training you’re on or you’re on your network so

you’re on or you’re on your network so

you’re on or you’re on your network so if you take a look at the links on the

if you take a look at the links on the

if you take a look at the links on the resources of this lesson that you can

resources of this lesson that you can

resources of this lesson that you can start trying out those products right

start trying out those products right

start trying out those products right now thank you so much yeah I got two

now thank you so much yeah I got two

now thank you so much yeah I got two questions Trulia

questions Trulia

questions Trulia first of all few steps back on the

first of all few steps back on the

first of all few steps back on the machine learning and neural networks

machine learning and neural networks

machine learning and neural networks mm-hmm

mm-hmm

mm-hmm you talked about more hidden layers to

you talked about more hidden layers to

you talked about more hidden layers to more complex algorithm yeah but Sturm

more complex algorithm yeah but Sturm

more complex algorithm yeah but Sturm x-fighters they’re like we were told in

x-fighters they’re like we were told in

x-fighters they’re like we were told in University to use yeah you’re feeling to

University to use yeah you’re feeling to

University to use yeah you’re feeling to see how many layers you have any tips on

see how many layers you have any tips on

see how many layers you have any tips on that or yeah yeah that’s actually a

that or yeah yeah that’s actually a

that or yeah yeah that’s actually a really good question

really good question

really good question so maybe question is is there any good

so maybe question is is there any good

so maybe question is is there any good practice on designing the Union neural

practice on designing the Union neural

practice on designing the Union neural networks right so as far as I know

networks right so as far as I know

networks right so as far as I know there’s no one theory to optimize your

there’s no one theory to optimize your

there’s no one theory to optimize your design of neural networks so everybody

design of neural networks so everybody

design of neural networks so everybody even in the fifth at Google you know

even in the fifth at Google you know

even in the fifth at Google you know when I asked the Google research team

when I asked the Google research team

when I asked the Google research team and people would say you know let’s

and people would say you know let’s

and people would say you know let’s start with the five 5 children

start with the five 5 children

start with the five 5 children let’s see how it works all right so

let’s see how it works all right so

let’s see how it works all right so strata and that’s the largest challenge

strata and that’s the largest challenge

strata and that’s the largest challenge we have right now for deploying the

we have right now for deploying the

we have right now for deploying the neural networks for your own data or

neural networks for your own data or

neural networks for your own data or applications so you have to do it in

applications so you have to do it in

applications so you have to do it in many many trials just right the the

many many trials just right the the

many many trials just right the the people in the pharmaceutical companies

people in the pharmaceutical companies

people in the pharmaceutical companies trying to create a new drug so you have

trying to create a new drug so you have

trying to create a new drug so you have to have a different combination of

to have a different combination of

to have a different combination of obably hyperparameters hyper parameters

obably hyperparameters hyper parameters

obably hyperparameters hyper parameters means the parameters such as the number

means the parameters such as the number

means the parameters such as the number of hidden layers or the or new ones or

of hidden layers or the or new ones or

of hidden layers or the or new ones or the way you can import the data or

the way you can import the data or

the way you can import the data or extracting features so you have to try

extracting features so you have to try

extracting features so you have to try out every different combinations that’s

out every different combinations that’s

out every different combinations that’s the problem

the problem

the problem and also it takes much much computation

and also it takes much much computation

and also it takes much much computation power yeah I think that’s what all do

power yeah I think that’s what all do

power yeah I think that’s what all do yeah it’s not a theory behind it and a

yeah it’s not a theory behind it and a

yeah it’s not a theory behind it and a little bit later in the presentation but

little bit later in the presentation but

little bit later in the presentation but use cases will be supported in the near

use cases will be supported in the near

use cases will be supported in the near future

future

future we’re going from post training to

we’re going from post training to

we’re going from post training to runtime can network we and B be exported

runtime can network we and B be exported

runtime can network we and B be exported in order to use a better processing for

in order to use a better processing for

in order to use a better processing for example what is second question cannon

example what is second question cannon

example what is second question cannon can the network we can be be exported in

can the network we can be be exported in

can the network we can be be exported in order to reuse in embedded processing

order to reuse in embedded processing

order to reuse in embedded processing for example by exporting okay yeah

for example by exporting okay yeah

for example by exporting okay yeah before the first question which is the

before the first question which is the

before the first question which is the the online training I think it’s on the

the online training I think it’s on the

the online training I think it’s on the world map or maybe your do list of the

world map or maybe your do list of the

world map or maybe your do list of the nucleus but it’s currently it’s not

nucleus but it’s currently it’s not

nucleus but it’s currently it’s not supported but it’s possible that we’ll

supported but it’s possible that we’ll

supported but it’s possible that we’ll be supporting the online training where

be supporting the online training where

be supporting the online training where your neural networks will be gradually

your neural networks will be gradually

your neural networks will be gradually joined by the online data and second

joined by the online data and second

joined by the online data and second question is exporting yes you can export

question is exporting yes you can export

question is exporting yes you can export the trained parameter sets so that you

the trained parameter sets so that you

the trained parameter sets so that you can use the parameter assists to your

can use the parameter assists to your

can use the parameter assists to your une use cases such as the importing the

une use cases such as the importing the

une use cases such as the importing the parameters into the IOT devices or maybe

parameters into the IOT devices or maybe

parameters into the IOT devices or maybe you can even copy that data sets into

you can even copy that data sets into

you can even copy that data sets into different cloud record AWS to learn your

different cloud record AWS to learn your

different cloud record AWS to learn your predictions on database ok those

predictions on database ok those

predictions on database ok those questions are cut do anyone else has any

questions are cut do anyone else has any

questions are cut do anyone else has any questions no and thank you very much

questions no and thank you very much

questions no and thank you very much thank you so much