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Eric S
Eric S
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I see from your comment you are most interested in what a machine learning technique consitutes. You could do worse than simply reading the Wikipedia article. I'm not an expert, but have dabbled in the implementation of machine learning in a field which I am an expert in.

In a nutshell, machine learning is a class of computer algorithms which use large amounts of example data to train a model which can then be used to predict a result from unknown data. For example you might want your model to be able to tell you if image contains a balloon. You train you algorithmyour model on perhaps 10,000 images which have been prejudged as to whether they have a balloon. Then test the model on another set of images where you know the answer to test the model. If all goes well, the model can then be used to detect balloons in images with high accuracy.

The training process for machine learning algorithms such as convolutional neural networks is very numerically intensive. They can be parallelized however which means that you can split the computation to run an many processors at once. Graphical Processing Units (GPRs) are comprised of large numbers of processors so can be used to speed up training dramatically. This particular patent is an example of one such method.

I see from your comment you are most interested in what a machine learning technique consitutes. You could do worse than simply reading the Wikipedia article. I'm not an expert, but have dabbled in the implementation of machine learning in a field which I am an expert in.

In a nutshell, machine learning is a class of computer algorithms which use large amounts of example data to train a model which can then be used to predict a result from unknown data. For example you might want your model to be able to tell you if image contains a balloon. You train you algorithm on perhaps 10,000 images which have been prejudged as to whether they have a balloon. Then test on another set of images where you know the answer to test the model. If all goes well, the model can then be used to detect balloons in images with high accuracy.

The training process for machine learning algorithms such as convolutional neural networks is very numerically intensive. They can be parallelized however which means that you can split the computation to run an many processors at once. Graphical Processing Units (GPRs) are comprised of large numbers of processors so can be used to speed up training dramatically. This particular patent is an example of one such method.

I see from your comment you are most interested in what a machine learning technique consitutes. You could do worse than simply reading the Wikipedia article. I'm not an expert, but have dabbled in the implementation of machine learning in a field which I am an expert in.

In a nutshell, machine learning is a class of computer algorithms which use large amounts of example data to train a model which can then be used to predict a result from unknown data. For example you might want your model to be able to tell you if image contains a balloon. You train your model on perhaps 10,000 images which have been prejudged as to whether they have a balloon. Then test the model on another set of images where you know the answer. If all goes well, the model can then be used to detect balloons in images with high accuracy.

The training process for machine learning algorithms such as convolutional neural networks is very numerically intensive. They can be parallelized however which means that you can split the computation to run an many processors at once. Graphical Processing Units (GPRs) are comprised of large numbers of processors so can be used to speed up training dramatically. This particular patent is an example of one such method.

Source Link
Eric S
Eric S
  • 11.7k
  • 2
  • 14
  • 34

I see from your comment you are most interested in what a machine learning technique consitutes. You could do worse than simply reading the Wikipedia article. I'm not an expert, but have dabbled in the implementation of machine learning in a field which I am an expert in.

In a nutshell, machine learning is a class of computer algorithms which use large amounts of example data to train a model which can then be used to predict a result from unknown data. For example you might want your model to be able to tell you if image contains a balloon. You train you algorithm on perhaps 10,000 images which have been prejudged as to whether they have a balloon. Then test on another set of images where you know the answer to test the model. If all goes well, the model can then be used to detect balloons in images with high accuracy.

The training process for machine learning algorithms such as convolutional neural networks is very numerically intensive. They can be parallelized however which means that you can split the computation to run an many processors at once. Graphical Processing Units (GPRs) are comprised of large numbers of processors so can be used to speed up training dramatically. This particular patent is an example of one such method.