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

audio audioduration (s) 0.62 28.4	text stringlengths 2 287
	with neural networks is of the way that ah we would be starting down understanding as
	comes into it but before starting down any of these aspects over there the first introduction
	which we need to have very clear in our minds is about what do we define as something called
	as learning now if you go down by the very classical definition
	on machine learning is ah what outlines it out and the outline is something like this
	that a computer program is said to learn from certain experience e with respect to a certain
	class of task t and a performance measure p so if you see there are three attributes
	factor which is called as an experience e there is a particular task which it has to
	perform t and there is a performance measure p
	classification right so we are not doing any other task so this this was like if i want
	to just find out whether there is a ball in the image or there isnt a ball then thats
	as my experience was increasing which is my number of epochs over which i was translating
	that means that i am somehow able to measure my performance and see that the performance
	is increasing so as the performance increases it becomes more and more accurate and accordingly
	to learn if this performance on a task t which is of my classification as measured by this
	learning is all centered around in fact human learning is also quite similar there also
	as as human beings when we say that we are learning about something then the whole task
	of learning is when we are able to really getting more
	standard definition now once we have been able to define that one lets look into trying
	to demystify what this would mean now lets get down with a very basic problem
	have you been what have you seen what have you learned whatever you experience and then
	humankind gains over there so if this is the image which is given down
	even there i mean this this blind person to somehow know and contemplate on your own experience
	of this one or a very simple thing which is called as an image captioning problem as of
	today so what will happen is something like this
	that as you would see that initially a computer program it if it is a so it will be doing
	over there what you would see is that as its able to understand and recognize each block
	increasing along that one now going down through that one what happens is that in the next
	instant that it will be able to identify some more objects over there and they are those
	and there is a great wall tower and finally it can identify these different
	equivalent of whatever it has identified over here now the interesting aspect which happens
	to that experience now as it took all of this a good amount of time coming down over there
	to the sentence so there is a lot of error so finally when you go closely on the sentence
	now now that we know that this is what essentially we meant down when we were saying down that
	its learning something the next objective is to understand what was it learning and
	how was it learning more than what it is actually how does it actually go on to learn this one
	so lets again get back over there so as you see in the whole image you would be getting
	down the image first and then the first objective over there is to break it down into some number
	it comes and lets say that this is breaking down an image into its salient segments now
	to identify some of these segments or what is also called as an objectification task
	then the machine is able to find out that there are certain number of inanimate objects
	that there are humans over there it will try to recognize humans find out who is who actually
	and this is essentially what what this machine is able to do but the question is even bigger
	the question is that we know that how it was learning was by doing something of this sort
	and the deeper it keeps on going so that is over the hierarchy as it keeps on going which
	keeps on climbing climbing climbing up to the description of the scene so as it keeps
	it and now the aspect of deep learning says that
	as its able to go down so its obviously gaining this depth by gaining looking at more number
	of images getting down more and more experience and accordingly its its performance is increasing
	the major question which we have as of now so ah i would give you a few seconds to actually
	ponder on this one whether its unique or not do you think there can be a non unique way
	would make a replica of this itself now let me just remove certain of these connections
	these blocks is still the same whereas the order in which the blocks were connected somehow
	here as you look into over here what happens is that you can still put down image it will
	goes on to recognize and some of you can even say that we can pull
	the inanimate things over there and thats thats perfectly fine i mean thats also another
	possibility of doing it so as you see what happens is essentially it turns out that there
	so as researchers for us its a very interesting point because we know that there can be multiple
	a product development perspective its really really a very dicey situation because if you
	have non unique ways of solving a problem that means you will have to explode down each
	and every possibility of solving out that problem and find out which is the best possible
	solution in order to achieve a solution to this problem
	only way of doing and and and this this problem this this challenge which we have over here
	way of solving this problem and yet more another way of in fact there are certain interesting
	papers which do come out in conferences which called as yet another way of solving this
	but then the point is is that the only issue which comes out or or can there be some other
	ways of doing it as well so as it turns out this is not the only challenge
	on this small block itself ok so that should be enough to say whether there is so this
	of a body part recognition or run down one classifier which can identify which which
	and this is a body part recognition now once i have my body part recognition what you can
	do is between these body parts i can draw down lines and find out what are the distance
	these distance relationships the angles in which they vary and then using that these
	are very pretty different over there then the posture because i they they dont always
	different the posture is different the distance between the legs and the hands are different
	the the angles at which these things are connected they are also pretty different and thats what
	which is like if human beings are present over there in black and whites this is what
	ways and two non unique ways of detecting humans and as it turns out that you can have
	speech and and apparently it turns out that there is no unique way of doing it
	processing which is from your sentences can you make inferences out of it or say today
	it knows that it has to put down get todays date and generate a query to our website on
	now from there there are interesting problems on hierarchical and transfer learning as well
	and so we would eventually go down a bit later on into what this transfer learning and hierarchical
	learning is all about and it it does exist in the field of medical imaging and image
	scope for researchers was for a longer duration of time but today if you see with the advent
	what we come down to is lets come down to the most consistent solution available by
	dilemma and for that whats done is something of that sort so say you have this image captioning
	problem over there so what i can do is i can take an image i can organize all the pixels
	subsequent nodes over there and now finally what it would do is that there is it would
	generate some sort of an output which would say that there is a great wall behind and
	there and if you look into this one what what this
	the pixels and from pixels it will translate to some alternate representations by clubbing
	all the pixels together into one representation than another and then subsequently as it goes
	certain labels over there now carefully getting back this model actually
	on on simple neural networks so we will get down into exactly what how the mathematics
	layers and each of this is what is called as a hidden layer the reason its hidden is
	of these layers like its its no target output which comes out the target output only comes
	so these output layer and the input layer to which you give an input and you draw an
	output from is what is called as the visible layers and inside all of these intermediate
	as the hidden layers over there now as you get a multi layer perceptron what comes down
	is that you will also have to train a multi layer perceptron

with neural networks is of the way that ah we would be starting down understanding as

comes into it but before starting down any of these aspects over there the first introduction

which we need to have very clear in our minds is about what do we define as something called

as learning now if you go down by the very classical definition

on machine learning is ah what outlines it out and the outline is something like this

that a computer program is said to learn from certain experience e with respect to a certain

class of task t and a performance measure p so if you see there are three attributes

factor which is called as an experience e there is a particular task which it has to

perform t and there is a performance measure p

classification right so we are not doing any other task so this this was like if i want

to just find out whether there is a ball in the image or there isnt a ball then thats

as my experience was increasing which is my number of epochs over which i was translating

that means that i am somehow able to measure my performance and see that the performance

is increasing so as the performance increases it becomes more and more accurate and accordingly

to learn if this performance on a task t which is of my classification as measured by this

learning is all centered around in fact human learning is also quite similar there also

as as human beings when we say that we are learning about something then the whole task

of learning is when we are able to really getting more

standard definition now once we have been able to define that one lets look into trying

to demystify what this would mean now lets get down with a very basic problem

have you been what have you seen what have you learned whatever you experience and then

humankind gains over there so if this is the image which is given down

even there i mean this this blind person to somehow know and contemplate on your own experience

of this one or a very simple thing which is called as an image captioning problem as of

today so what will happen is something like this

that as you would see that initially a computer program it if it is a so it will be doing

over there what you would see is that as its able to understand and recognize each block

increasing along that one now going down through that one what happens is that in the next

instant that it will be able to identify some more objects over there and they are those

and there is a great wall tower and finally it can identify these different

equivalent of whatever it has identified over here now the interesting aspect which happens

to that experience now as it took all of this a good amount of time coming down over there

to the sentence so there is a lot of error so finally when you go closely on the sentence

now now that we know that this is what essentially we meant down when we were saying down that

its learning something the next objective is to understand what was it learning and

how was it learning more than what it is actually how does it actually go on to learn this one

so lets again get back over there so as you see in the whole image you would be getting

down the image first and then the first objective over there is to break it down into some number

it comes and lets say that this is breaking down an image into its salient segments now

to identify some of these segments or what is also called as an objectification task

then the machine is able to find out that there are certain number of inanimate objects

that there are humans over there it will try to recognize humans find out who is who actually

and this is essentially what what this machine is able to do but the question is even bigger

the question is that we know that how it was learning was by doing something of this sort

and the deeper it keeps on going so that is over the hierarchy as it keeps on going which

keeps on climbing climbing climbing up to the description of the scene so as it keeps

it and now the aspect of deep learning says that

as its able to go down so its obviously gaining this depth by gaining looking at more number

of images getting down more and more experience and accordingly its its performance is increasing

the major question which we have as of now so ah i would give you a few seconds to actually

ponder on this one whether its unique or not do you think there can be a non unique way

would make a replica of this itself now let me just remove certain of these connections

these blocks is still the same whereas the order in which the blocks were connected somehow

here as you look into over here what happens is that you can still put down image it will

goes on to recognize and some of you can even say that we can pull

the inanimate things over there and thats thats perfectly fine i mean thats also another

possibility of doing it so as you see what happens is essentially it turns out that there

so as researchers for us its a very interesting point because we know that there can be multiple

a product development perspective its really really a very dicey situation because if you

have non unique ways of solving a problem that means you will have to explode down each

and every possibility of solving out that problem and find out which is the best possible

solution in order to achieve a solution to this problem

only way of doing and and and this this problem this this challenge which we have over here

way of solving this problem and yet more another way of in fact there are certain interesting

papers which do come out in conferences which called as yet another way of solving this

but then the point is is that the only issue which comes out or or can there be some other

ways of doing it as well so as it turns out this is not the only challenge

on this small block itself ok so that should be enough to say whether there is so this

of a body part recognition or run down one classifier which can identify which which

and this is a body part recognition now once i have my body part recognition what you can

do is between these body parts i can draw down lines and find out what are the distance

these distance relationships the angles in which they vary and then using that these

are very pretty different over there then the posture because i they they dont always

different the posture is different the distance between the legs and the hands are different

the the angles at which these things are connected they are also pretty different and thats what

which is like if human beings are present over there in black and whites this is what

ways and two non unique ways of detecting humans and as it turns out that you can have

speech and and apparently it turns out that there is no unique way of doing it

processing which is from your sentences can you make inferences out of it or say today

it knows that it has to put down get todays date and generate a query to our website on

now from there there are interesting problems on hierarchical and transfer learning as well

and so we would eventually go down a bit later on into what this transfer learning and hierarchical

learning is all about and it it does exist in the field of medical imaging and image

scope for researchers was for a longer duration of time but today if you see with the advent

what we come down to is lets come down to the most consistent solution available by

dilemma and for that whats done is something of that sort so say you have this image captioning

problem over there so what i can do is i can take an image i can organize all the pixels

subsequent nodes over there and now finally what it would do is that there is it would

generate some sort of an output which would say that there is a great wall behind and

there and if you look into this one what what this

the pixels and from pixels it will translate to some alternate representations by clubbing

all the pixels together into one representation than another and then subsequently as it goes

certain labels over there now carefully getting back this model actually

on on simple neural networks so we will get down into exactly what how the mathematics

layers and each of this is what is called as a hidden layer the reason its hidden is

of these layers like its its no target output which comes out the target output only comes

so these output layer and the input layer to which you give an input and you draw an

output from is what is called as the visible layers and inside all of these intermediate

as the hidden layers over there now as you get a multi layer perceptron what comes down

is that you will also have to train a multi layer perceptron