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After months learning about machine learning for time series forecasting, several chapters in a book on deep learning techniques for time series analysis and fo

After months learning about machine learning for time series forecasting, several chapters in a book on deep learning techniques for time series analysis and forecasting, the author kindly pointed out that there are no papers published up to that point that prove deep learning (neural networks) can perform better than classical statistics. From the scikit-learn faqs: > Will you add GPU support? > No, or at least not in the near future. The main reason is that GPU support will introduce many software dependencies and introduce platform specific issues. scikit-learn is designed to be easy to install on a wide variety of platforms. Outside of neural networks, GPUs don’t play a large role in machine learning today, and much larger gains in speed can often be achieved by a careful choice of algorithms. Of course, there are libraries that can support GPU acceleration for numpy calculations using matrix transformations now. Nonetheless, they are not often necessary.

gwbas1c 5y ago
> the author kindly pointed out that there are no papers published up to that point that prove deep learning (neural networks) can perform better than classical statistics. Early in my career I moved to Silicon Valley to work for a large company. The project was a machine learning project. I was taking models defined in XML, grabbing data from a few different databases, and running it through a machine learning engine written in-house. After a year and a half, it came out that our machine-learning-based system couldn't beat the current system that used normal statistics. What rubbed me the wrong way was that the managers brought someone else in to run the data, manually, through the machine learning algorithm. More specifically, what bothered me was that we didn't attempt this kind of experiment early in the project. It felt like I was hired to work on a "solution in search of a problem." Career lesson: Ask a lot of questions early in a project's life. If you're working on something that uses machine learning, ask what system it's replacing, and make sure that someone (or you) runs it manually before spending the time to automate.

gwbas1c 5y ago

> the author kindly pointed out that there are no papers published up to that point that prove deep learning (neural networks) can perform better than classical statistics. Early in my career I moved to Silicon Valley to work for a large company. The project was a machine learning project. I was taking models defined in XML, grabbing data from a few different databases, and running it through a machine learning engine written in-house. After a year and a half, it came out that our machine-learning-based system couldn't beat the current system that used normal statistics. What rubbed me the wrong way was that the managers brought someone else in to run the data, manually, through the machine learning algorithm. More specifically, what bothered me was that we didn't attempt this kind of experiment early in the project. It felt like I was hired to work on a "solution in search of a problem." Career lesson: Ask a lot of questions early in a project's life. If you're working on something that uses machine learning, ask what system it's replacing, and make sure that someone (or you) runs it manually before spending the time to automate.