Difference between revisions of "End-to-end learning"

From Computer Science Wiki
Jump to navigation Jump to search
Line 14: Line 14:
== How does it work or a deeper look ==
== How does it work or a deeper look ==


End-to-end learning can be separated into '''two''' major parts(similarly to [[Deep_learning]]). '''Training'''<ref>https://developer.nvidia.com/deep-learning</ref> is the first phase, in which the machine records all of the all of the parameters human operator uses in what sort of situations(accessed by [[Convolutional neural networks (CNNs)]]). '''Inference'''<ref>https://developer.nvidia.com/deep-learning</ref> then is possible, with the machine acting upon previously gained experience from the training phase of the End-to-end learning.
End-to-end learning process can be separated into '''two major components''' (similarly to [[Deep_learning]] process). '''Training component'''<ref>https://developer.nvidia.com/deep-learning</ref> is the first phase, in which the machine records all of the parameters execurted by the human operator (through [[Convolutional neural networks (CNNs)]]). '''Inference component'''<ref>https://developer.nvidia.com/deep-learning</ref> then is possible, with the machine acting upon previously gained experience from the '''Training component''' of the end-to-end learning process.


The only difference between end-to-end learning and [[Deep_learning]] processes is that the end-to-end learning must collect the parameters jointly(at the same time), while [[Deep_learning]] can collect the parameters jointly or step by step. Therefore, every end-to-end learning is [[Deep_learning]] process , but not every [[Deep_learning]] process is step by step learning.
The only difference between end-to-end learning process and [[Deep_learning]] process is that the end-to-end learning process must collect all of the parameters jointly(at the same time), while [[Deep_learning]] process can collect the parameters ether jointly or step by step. Therefore, every end-to-end learning process is [[Deep_learning]] process , but not every [[Deep_learning]] process is end-to-end learning process.


== Examples ==
== Examples ==


End-to-end learning is specifically prevalent in the autonomous cars industry(our 2018's case study), as this process benefits fit perfectly with the car's [[Convolutional neural networks (CNNs)]]. As the autonomous car receives multiple parameters through [[Convolutional neural networks (CNNs)]] at the same time, it is beneficial to use end-to-end learning which is able to '''Train''' or '''Infer''' upon them.
End-to-end learning process is specifically prevalent in the autonomous cars industry(our 2018's case study), as this process benefits fit perfectly with the car's [[Convolutional neural networks (CNNs)]]. As the autonomous car jointly receives multiple parameters through [[Convolutional neural networks (CNNs)]], it is beneficial to use end-to-end learning process which is able to '''Train''' or '''Infer''' upon the parameters.


For example, the autonomous car "turns right to the compund", as there is a smaller speed limit the car needs to adjust its speed accordingly, while at the same time the car actually needs to turn as well. In this situation end-to-end learning lets the car execute the correct '''Inference''' based upon multiple receive parameters.
One example could be: "the autonomous car is required to turn right to the civilized area from a highway", as there is a certain speed limit the car needs to adjust its speed accordingly, while at the same time the car needs to turn to the right as well. In this situation end-to-end learning lets the car execute the correct '''Inference''' based upon multiple receive parameters.




== Pictures, diagrams ==
== Pictures, diagrams ==
As one can see the masterfully edited picture in paint by the true paint prodigy on the right. The circled parameters are assessed jointly(at the same time), while the entire thing still remains to be [[Deep_learning]].
As one can see the masterfully edited picture in paint by the true paint prodigy on the right. The circled parameters are assessed jointly(at the same time), while the entire thing still remains to be [[Deep_learning]] process. As the received parameters are assessed jointly within this [[Deep_learning]] process, this process can be classified as a end-to-end learning process as well.
As the received parameters are assessed jointly within this [[Deep_learning]], this process can be classified as end-to-end learning as well.





Revision as of 02:07, 14 July 2017

Exclamation.png This is student work which has not yet been approved as correct by the instructor WIP need to check grammar and structure, other than that i have done everything

Case study notes[1]

Introduction[edit]

End-to-end learning process is a type of Deep_learning process in which all of the parameters are trained jointly, rather than step by step. [2] Furthermore, just like in the case of Deep_learning precess, in end-to-end learning process the machine uses previously gained human input, in order to execute its task.[3] This proces is specyfcly prevelant in the auntonomous cars industry(our 2018's case study), as this proces's benefites fitt perfectly with the car's Convolutional neural networks (CNNs).

How does it work or a deeper look[edit]

End-to-end learning process can be separated into two major components (similarly to Deep_learning process). Training component[4] is the first phase, in which the machine records all of the parameters execurted by the human operator (through Convolutional neural networks (CNNs)). Inference component[5] then is possible, with the machine acting upon previously gained experience from the Training component of the end-to-end learning process.

The only difference between end-to-end learning process and Deep_learning process is that the end-to-end learning process must collect all of the parameters jointly(at the same time), while Deep_learning process can collect the parameters ether jointly or step by step. Therefore, every end-to-end learning process is Deep_learning process , but not every Deep_learning process is end-to-end learning process.

Examples[edit]

End-to-end learning process is specifically prevalent in the autonomous cars industry(our 2018's case study), as this process benefits fit perfectly with the car's Convolutional neural networks (CNNs). As the autonomous car jointly receives multiple parameters through Convolutional neural networks (CNNs), it is beneficial to use end-to-end learning process which is able to Train or Infer upon the parameters.

One example could be: "the autonomous car is required to turn right to the civilized area from a highway", as there is a certain speed limit the car needs to adjust its speed accordingly, while at the same time the car needs to turn to the right as well. In this situation end-to-end learning lets the car execute the correct Inference based upon multiple receive parameters.


Pictures, diagrams[edit]

As one can see the masterfully edited picture in paint by the true paint prodigy on the right. The circled parameters are assessed jointly(at the same time), while the entire thing still remains to be Deep_learning process. As the received parameters are assessed jointly within this Deep_learning process, this process can be classified as a end-to-end learning process as well.


Training-624x291.png

External links[edit]

  • It would be helpful
  • to include many links
  • to other internet resources
  • to help fellow students
  • Please make sure the content is good
  • and don't link to a google search results, please

References[edit]