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It is fair to say KANN "only applicable for rather small problems". However, KANN is not so "similar" to Tinn. Tinn only implements MLP with a single hidden lay
by attractivechaos 9y ago
It is fair to say KANN "only applicable for rather small problems". However, KANN is not so "similar" to Tinn. Tinn only implements MLP with a single hidden layer. KANN supports a lot more features such as 1D/2D convolution, RNN/LSTM, weight sharing and mini-batching. It is also more optimized.
- stochastic_monk 9y agoYou're absolutely right that kann is more optimized and full-featured. I thought to mention your library because it is also a very portable neural network library written in C. kann is a stunning piece of engineering, and your code is magnificent, and that is precisely why I mentioned it. I apologize, I did not clearly express my intent.
- attractivechaos 9y agoI just meant to add some clarification. I actually do appreciate that you mentioned it. Thank you!