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How to implement a neural network: Part 1
- deleted 11y ago[deleted]
- interdrift 11y agoI'm doing the Coursera machine learning course. You guys can try it too : https://www.coursera.org/learn/machine-learning https://www.coursera.org/learn/machine-learning . It's very relevant to this topic. Good explanation is presented here too.
- solomatov 11y agoAFAIU, it's a tutorial on old neural networks, not on deep learning.
- yzh 11y agoWhat do you mean by old neural networks?
- solomatov 11y agoI mean neural network algorithms which were used prior to discovery of deep learning methods.
- ajtulloch 11y agoThe algorithms haven't (fundamentally) changed.
- solomatov 11y agoThey have changed, otherwise we didn't have a breakthrough.
- deleted 11y ago[deleted]
- deleted 11y ago[deleted]
- tjradcliffe 11y agoBarely at all. Long Short-Term Memory network blocks, for example, are basically perceptrons with feedback controlled by two or three completely conventional non-linear nets running off the same inputs. Deep learning networks are an assembly of existing connectionist parts, enabled by Moore's Law. There's nothing bad about this: the progress being made is real and substantial, but it's being made in ways that are true to Edison's prescribed ratio of inspiration and perspiration, although a lot of the perspiring is being done by machines. The architectural tweaks, while small, are still very important. But the core learning algorithm is still backprop deployed over the new architectures.
- dr_zoidberg 11y agoI agree, I've seen someone go as far as calling DNN's "just a big backpropagated multilayer perceptron, with absurd ammounts of neurons and layers, enabled only by the processing power of GPUs". It's way too simplified, but I did see the point behind the argument.
- chestervonwinch 11y agoYou know - the ones you had to start with a crank.
- p1esk 11y agoYou made my day :)