Re:How to Learn Machine Learning and Deep Learning: A Guide ...
by ilaksh on Tuesday December 15, 2020 @08:33PM
(#25437948)
It seems cutting-edge how? Because for example you can literally go to GitHub and get a project with a complex deep learning architecture close to the state of the art (or at least last year's state of the art) for some things.
Then pull that up on your screen and start messing with the structure of the graph, adding layers etc. with impressive sounding names like "relu" etc. Lol. It could look cutting edge and it actually could kind of be cutting edge as far as the fact that regardless of your background the next step to making an incremental improvement in an area may be taking a known model and tweaking the hyperparameters and assembling a good dataset.
But obviously there is a difference between that and creating a new architecture or approach from scratch. In other words, you are not going to invent GANs or capsules by tweaking hyperparameters. Or come up with other more basic innovations necessarily without a deep understanding of the underlying concepts.
However I will say that the repeated practical application of deep learning seems like it is going to be a critical part of a having a strong grasp. And someone with a Ph.D in ML mifht actually be less useful for a particular application than a person who has actually used Keras for ten different real projects.
At the core, many people are involved in applying existing approaches by assembling datasets and tweaking hyperparameters and architectures. Which maybe amounts to modifying a few hundred or maybe dozen lines of Python code that describe the different layers and learning rates etc. and load the data.
People can leverage programming libraries and be more effective than another guy who has less experience with that library but is a true expert in statistics and LaTeX.
Then pull that up on your screen and start messing with the structure of the graph, adding layers etc. with impressive sounding names like "relu" etc. Lol. It could look cutting edge and it actually could kind of be cutting edge as far as the fact that regardless of your background the next step to making an incremental improvement in an area may be taking a known model and tweaking the hyperparameters and assembling a good dataset.
But obviously there is a difference between that and creating a new architecture or approach from scratch. In other words, you are not going to invent GANs or capsules by tweaking hyperparameters. Or come up with other more basic innovations necessarily without a deep understanding of the underlying concepts.
However I will say that the repeated practical application of deep learning seems like it is going to be a critical part of a having a strong grasp. And someone with a Ph.D in ML mifht actually be less useful for a particular application than a person who has actually used Keras for ten different real projects.
At the core, many people are involved in applying existing approaches by assembling datasets and tweaking hyperparameters and architectures. Which maybe amounts to modifying a few hundred or maybe dozen lines of Python code that describe the different layers and learning rates etc. and load the data.
People can leverage programming libraries and be more effective than another guy who has less experience with that library but is a true expert in statistics and LaTeX.