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This looks good. I mostly use deep learning for everything, while this project nicely automates non-deep learning ML. When I managed a deep learning team at Ca
by mark_l_watson 4y ago
This looks good. I mostly use deep learning for everything, while this project nicely automates non-deep learning ML.
When I managed a deep learning team at Capital One, my last technical project was automatic deep learning architecture search. I think that the fields of data engineering, data science, machine learning, and deep learning are all all ripe for massive automation, reducing the number of jobs in these fields. I think that there will be accelerated use of all of these fields, just much less manual work will be required.
- sebastiansm 4y agoAt least for tabular data there is no clear evidence that a neural network is superior than a tree-based model [0]. Many Data Scientist colleagues go straight to neural networks thinking that they will have a better result. Considering the complexity and computational cost, I think it is convenient to evaluate which tool to use for each case. [0] https://sebastianraschka.com/blog/2022/deep-learning-for-tabular-data.html https://sebastianraschka.com/blog/2022/deep-learning-for-tab...
- mark_l_watson 4y agoFive years ago, I trained a GAN deep model to synthesize data based a tabular training data. Colleagues extended thus to also handle categorical and string data. Models trained on synthetic data performed well in withheld real data. I mention this as some evidence that deep models subsume conventional ML.