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As someone who utilizes machine learning but doesn't have a great understanding of neural networks, I've not had good experiences with TensorFlow. Myself and a
by alex_g 9y ago
As someone who utilizes machine learning but doesn't have a great understanding of neural networks, I've not had good experiences with TensorFlow. Myself and a friend attempted to use it for a project but found a lack of examples and the documentation to be very unclear. Even doing basic things for which tutorials did exist presented us with lots of warnings and errors. Debugging one of these, for example, led us to believe that certain functions were incomplete or abandoned, without clear messaging in the documentation that this was he case.
For those interested in diving into machine learning, I'd recommend not starting with neural networks. I think there's a belief amongst those unfamiliar that a neural net will be 10x better than anything else, but this is not always the case, and you can accomplish a lot with simple functionality found in scikit.
TLDR, glad to see more tutorials!
- dagw 9y agoAgreed about scikit-learn. Anecdotally I've found that most "deep learning" and "big data" problems I see in industry could be trivially solved using scikit-learn on a reasonably powerful laptop. Also if you want to really understand neural networks I can recommend Andrew Ng's new deep learning course on Coursea. You start with deriving the underlying math and implementing a simple (but fully functional) neural network using just basic numpy, so that when you get to tensor flow and the latest cutting edge techniques you actually understand what's happening under the hood and how things actually work.