SPRINGLab/asr-task-data · Datasets at Hugging Face

audio audioduration (s) 0.62 28.4	text stringlengths 2 287
	but my point is that every time you have a different combination of initial weights given
	get its features extracted on each of the small segments within that image and then
	is done only then you can go down to the layer of human record appearance module
	and thats what will associate itself to one of these bits over there now as it turns out
	if you can go down to a different initialization you will have a different model or doing it
	of a model and this is the major reason why you really have a trouble or a major issue
	there can be and subsequently we will enter eventually into the math of trying to solve
	networks over here essentially are that they are not something new
	so around in the time of nineteen sixties there were some more interesting things which
	started happening so initially till around the year of nineteen fifties what was going
	the whole objective was can you find on whether this whole mathematical model of a neural
	network has some sort of an analogy or does provide a plausible explanation of how biological
	another living organism so thats what was going down in nineteen sixty so the first
	few hidden layers over there they would be what are responsive to more of edges and complex
	recognition which happens in order to make us recognize a particular object and then
	and thats the standard multi layer perceptron which we are looking over here and which we
	and these the first theories which were being proposed on with this kind of an association
	structures but then within the biological system and within our bodies ah they are not
	fully connected but they are sort of like what is called as a convolutional
	so instead of so if you remember clearly in the first weeks lecture on neural network
	is a unique weight which is associated with one neuron and associates to another neuron
	over there ah then we got down into something called as a weight replication which is across
	weights this is what it came down and as we go into more understanding of these deeper
	the cost function with respect to the weights of the network now when we try to solve this
	layer perceptron that it will be going down across the different depth layers so from
	almost close to thirty years as of now so going down from there is more things which
	came down in nineteen eighties to two thousand and this was a point where we had even more
	you have a complex problem to solve you would not like to solve it from start to end but
	then go down by a certain route and then keep on solving it out one at a time
	so its like breaking down a bigger complex problem into through multiple number of smaller
	problems over there then came down unsupervised pre training or what we would also be doing
	as auto encoders subsequently and then as ah as we go down in the next few lectures
	and then understanding what is the relationship between a multi layer perceptron and an auto
	where you need a lot of compute power and then around this time is when this compute
	power software libraries implementations and data sets and and you definitely need a huge
	so today if you solve a deep neural network you can pretty much train a very complex model
	and thats one of the prime reasons why deep learning was
	from just mired computer graphics generation or some some of this mesh grid like solvers
	for multi physics or physical simulations to getting down more of a compute centric
	thing and getting down architectures of memory interfacing data transfers which are something
	which are analogous to support down this high bandwidth requirement within ah neural networks
	for their implementation for data transfers because if you clearly see i have one layer
	and then via certain number of weights i connected to the other layer so each of these layers
	require certain memory and this operation in order for it to happen it will require
	a lot of memory transfer so whenever i do a x into w i would x one into w one so there
	going down over there and this is from a very heavy volume ram so basically your cpu to
	what led down to the advent as of now so from there on two thousand nine to was a gpu implementation
	belief networks working down and then in two thousand eleven came down the max pooling
	get down
	addressed and referenced down by the software libraries directly for the best access and
	alex net of two thousand twelve which is the one which so this was the first deep learning
	model which was beating down any of the classical models for filling the image net challenge
	so this is more of the history and in the subsequent classes we would be touching down
	on one single attribute of this history one single model and then see how this has contributed
	so one of them is the fully connected networks within this fully connected networks comes
	denoising as well as convolutional so convolutional auto encoder is some sort of a relationship
	of it so if i have a pattern x i would somehow encode it through certain weights in order
	to get down the same pattern x as the output now essentially you would see that well it
	so if my hidden layers keep on getting smaller and smaller than my input layer or my output
	i can get down a hidden layer of hundred neurons and if i am able to with through this network
	we will come down to those examples as well of how to get down an image compression as
	well running down with these neural networks ah then the next one is what is called as
	widely within the community so this is where you have some sort of a boltzmann distribution
	any input you can get an output or given
	so and input outputs are not so predefined over here it is just a pair of x and y so
	distributed and then when you stack them one on top of the other that is what leads to
	something called as a deep belief network so this is where all inputs all outputs and
	all intermittent are ones are directly connected when you change all of these direct connections
	down then these kind of networks are what is called as convolutional networks and or
	on the first few operational layers in terms of convolutions itself and are typically defined
	as convolutional networks
	operates on the time space itself so and its also called as a recurrent neural network
	so what happens is that the output of the neuron gets added down to the input of the
	neuron in the next time step so not in the same time step so if you i am processing down
	phones if you if you just write start typing a message after one alphabet it starts showing
	you a few alphabets or or even words over there and as you see as you keep on typing
	to the exact word ok
	if you see over there it it those black and white dots over there are basically some neuron
	outputs of a restricted boltzmann machine so as it generates a boltzmann distributed
	there you can generate a whole human face looking down and every time it does generate
	kind of deep neural networks in order to synthesize different facial expressions so as we go down
	or not so thats thats what has been building up on top of the years of corpus you have
	built by tagging your individual faces so in the initial days if you remember so that
	down your faces or or your friends over there and that was helping them create a large corpus
	and eventually initially those boxes were all fixed size square boxes eventually they
	coming up
	which this particular kind of technology or deep learning is helping us achieve in a real
	browser side so and it was really a fun to watch out so more about them is with this
	like amazon also have launched it out and thats about where you can take an image of
	catalogs and gives you the product catalog category on the on their e store and you can
	buy that sort of a dress so this is where its going down on impacting the consumer space
	as well so from there you see a huge ah aspect of going it into self driving cars and then
	autonomous driving full enormous mobility and not much left behind is microsoft thing
	so somewhere in two thousand fourteen they started up getting this public release
	as your assistant for pc systems so they are like really building up huge in terms of it
	apps anything which you are developing and what this can do is given an image it can
	these kind of things so this is what what is becoming increasingly deep learning powered
	an interesting observation was that
	this this whole thing of deep learning is quite like quantum physics at the beginning
	and based on practitioners and software coders ah these experiments have been far ahead of

but my point is that every time you have a different combination of initial weights given

get its features extracted on each of the small segments within that image and then

is done only then you can go down to the layer of human record appearance module

and thats what will associate itself to one of these bits over there now as it turns out

if you can go down to a different initialization you will have a different model or doing it

of a model and this is the major reason why you really have a trouble or a major issue

there can be and subsequently we will enter eventually into the math of trying to solve

networks over here essentially are that they are not something new

so around in the time of nineteen sixties there were some more interesting things which

started happening so initially till around the year of nineteen fifties what was going

the whole objective was can you find on whether this whole mathematical model of a neural

network has some sort of an analogy or does provide a plausible explanation of how biological

another living organism so thats what was going down in nineteen sixty so the first

few hidden layers over there they would be what are responsive to more of edges and complex

recognition which happens in order to make us recognize a particular object and then

and thats the standard multi layer perceptron which we are looking over here and which we

and these the first theories which were being proposed on with this kind of an association

structures but then within the biological system and within our bodies ah they are not

fully connected but they are sort of like what is called as a convolutional

so instead of so if you remember clearly in the first weeks lecture on neural network

is a unique weight which is associated with one neuron and associates to another neuron

over there ah then we got down into something called as a weight replication which is across

weights this is what it came down and as we go into more understanding of these deeper

the cost function with respect to the weights of the network now when we try to solve this

layer perceptron that it will be going down across the different depth layers so from

almost close to thirty years as of now so going down from there is more things which

came down in nineteen eighties to two thousand and this was a point where we had even more

you have a complex problem to solve you would not like to solve it from start to end but

then go down by a certain route and then keep on solving it out one at a time

so its like breaking down a bigger complex problem into through multiple number of smaller

problems over there then came down unsupervised pre training or what we would also be doing

as auto encoders subsequently and then as ah as we go down in the next few lectures

and then understanding what is the relationship between a multi layer perceptron and an auto

where you need a lot of compute power and then around this time is when this compute

power software libraries implementations and data sets and and you definitely need a huge

so today if you solve a deep neural network you can pretty much train a very complex model

and thats one of the prime reasons why deep learning was

from just mired computer graphics generation or some some of this mesh grid like solvers

for multi physics or physical simulations to getting down more of a compute centric

thing and getting down architectures of memory interfacing data transfers which are something

which are analogous to support down this high bandwidth requirement within ah neural networks

for their implementation for data transfers because if you clearly see i have one layer

and then via certain number of weights i connected to the other layer so each of these layers

require certain memory and this operation in order for it to happen it will require

a lot of memory transfer so whenever i do a x into w i would x one into w one so there

going down over there and this is from a very heavy volume ram so basically your cpu to

what led down to the advent as of now so from there on two thousand nine to was a gpu implementation

belief networks working down and then in two thousand eleven came down the max pooling

get down

addressed and referenced down by the software libraries directly for the best access and

alex net of two thousand twelve which is the one which so this was the first deep learning

model which was beating down any of the classical models for filling the image net challenge

so this is more of the history and in the subsequent classes we would be touching down

on one single attribute of this history one single model and then see how this has contributed

so one of them is the fully connected networks within this fully connected networks comes

denoising as well as convolutional so convolutional auto encoder is some sort of a relationship

of it so if i have a pattern x i would somehow encode it through certain weights in order

to get down the same pattern x as the output now essentially you would see that well it

so if my hidden layers keep on getting smaller and smaller than my input layer or my output

i can get down a hidden layer of hundred neurons and if i am able to with through this network

we will come down to those examples as well of how to get down an image compression as

well running down with these neural networks ah then the next one is what is called as

widely within the community so this is where you have some sort of a boltzmann distribution

any input you can get an output or given

so and input outputs are not so predefined over here it is just a pair of x and y so

distributed and then when you stack them one on top of the other that is what leads to

something called as a deep belief network so this is where all inputs all outputs and

all intermittent are ones are directly connected when you change all of these direct connections

down then these kind of networks are what is called as convolutional networks and or

on the first few operational layers in terms of convolutions itself and are typically defined

as convolutional networks

operates on the time space itself so and its also called as a recurrent neural network

so what happens is that the output of the neuron gets added down to the input of the

neuron in the next time step so not in the same time step so if you i am processing down

phones if you if you just write start typing a message after one alphabet it starts showing

you a few alphabets or or even words over there and as you see as you keep on typing

to the exact word ok

if you see over there it it those black and white dots over there are basically some neuron

outputs of a restricted boltzmann machine so as it generates a boltzmann distributed

there you can generate a whole human face looking down and every time it does generate

kind of deep neural networks in order to synthesize different facial expressions so as we go down

or not so thats thats what has been building up on top of the years of corpus you have

built by tagging your individual faces so in the initial days if you remember so that

down your faces or or your friends over there and that was helping them create a large corpus

and eventually initially those boxes were all fixed size square boxes eventually they

coming up

which this particular kind of technology or deep learning is helping us achieve in a real

browser side so and it was really a fun to watch out so more about them is with this

like amazon also have launched it out and thats about where you can take an image of

catalogs and gives you the product catalog category on the on their e store and you can

buy that sort of a dress so this is where its going down on impacting the consumer space

as well so from there you see a huge ah aspect of going it into self driving cars and then

autonomous driving full enormous mobility and not much left behind is microsoft thing

so somewhere in two thousand fourteen they started up getting this public release

as your assistant for pc systems so they are like really building up huge in terms of it

apps anything which you are developing and what this can do is given an image it can

these kind of things so this is what what is becoming increasingly deep learning powered

an interesting observation was that

this this whole thing of deep learning is quite like quantum physics at the beginning

and based on practitioners and software coders ah these experiments have been far ahead of