neuraltalk

Adam (founder of skymind here). I'd like to say that we don't encourage neural networks as a black box like a lot of the startups out there. I also mainly advocate neural networks on unstructured data where the results are proven to be a significant improvement over other techniques. Many startups in the space would believe that you can have a simple GUI and you're somehow set to go. In my upcoming oreilly book Deep Learning: A Practioner's Approach, I go through a practical applications oriented view of neural networks that I think will help change things in the coming years. I'd also add that I've implemented every neural network architecture and allow people to mix and match. A significant result that many people are familiar with are the image scene description generators done by karpathy et.al[1]. Either way, unlike most our code is

I've been following along, and this course has been great so far. The author also has made the code for one of his publications about image captioning publicly available: https://github.com/karpathy/neuraltalk Along with various other nice work about neural networks: http://cs.stanford.edu/people/karpathy/convnetjs/started.htm... http://cs.stanford.edu/people/karpathy/recurrentjs/ http://karpathy.github.io/neuralnets/ Nice visualization about classifiers from the class (various svm, softmax): http://vision.stanford.edu/teaching/cs231n/linear-classify-d...