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Jrinne
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Jim, I'm a newbie in ML, I rely on others since I have very little first hand experience. We conducted a study last year with a data scientist with a relatively small data set and the NN training was taking days . I think he told me it would have taken a week+ on our hardware that was not that bad. We do have newer machines so might be a different story now. But the learning has to come down by orders of magnitude, so I don't know. We'll see. I will show him your post. Whatever works. I am not personally motivated to see P123 use neural nets. This is an interesting topic and others have shown interest in it. Ensibe uses it for profit it seems. For individuals Colab has a significant upgrade in its resources for for $10 per month. The free version claims to have access to GPUs (Graphics Processor Units). But I do not find the free version to be faster than my MacBook Pro. And make no mistake, it is an old MacBook Pro—with 2 cores (2015). Here is the link to Colab: Colab Colab IS TensorFlow and Google. Probably created to help a new generation learn TensorFlow so Google (who created TensorFlow) can recruit new people already using TensorFlow. So you would expect Colab to have some solution that works for most people. I have been able to create slow models that do not finish with a wide variety of ML algorithms. I am pretty good at doing that. The most important factor in a NN model that is already standardized is the optimization method in my experience. Using "Stochastic Gradient Descent" is the easiest method for creating a neural-net program that will not finish running in my experience. Some books tout Stochastic Gradient Descent as a method. I can see the advantages. But generally stick with Nadam or Adam, I would recommend. Nadam is an advanced algorithm that is effective at adjusting the learning rate. Going fast when it can and slower when it needs to. In my experience most people are more aware of deep learning than they are of boosting and most associate deep learning with AI. Neural nets will be a marketing tool if you can provide it even if it may not be better than XGBoost for most models. But it is also true that people can develop a TensorFlow model with the API now. Without paying the ten dollars a month at Colab in my experience. I think one can get TensorFlow to work on a variety of systems if you want to use it for marketing. But it is not that hard to create one that does not finish running for any ML model. FWIW. Jim From time to time you will encounter Luddites, who are beyond redemption. --de Prado, Marcos López on the topic of machine learning for financial applications |
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Edit 17 times,
last edit by
Jrinne
at Feb 24, 2021 11:43:20 AM
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