Just came across this new book all about deep learning. I have only had time to scan through it so far but it looks to cover a lot of ground that is often assumed elsewhere. If you want to know all about how regularized autoencoders and recurrent neural nets work (to pick random examples), this is the place.
I have a project that requires identifying sequences of signals and classifying them in various ways and I have been looking for good techniques that could be applied to the problem. I came across a paper on Deep Gaussian Processes. They are somewhat related to deep neural networks but have an advantage in requiring a lot less training data. Since the generation of high quality training data is a big issue with DNNs, this is quite appealing. There are some GitHub repos with Python code to make getting started easier. The screenshot is from a demo in the deepGPy repo. Hopefully it will do what I want but, at the very least, I am learning some new mathematics.
Found this very interesting paper on deep convolutional neural networks via a post on the MIT Technology Review web site. It describes a system using multiple GPUs to achieve pretty accurate image recognition. What’s even better, code is available here for multiple NVIDIA CUDA systems. I need to look at it in more detail but it looks like it has all the necessary config files to set up the neural network as described in the paper and would be a good starting point for other uses.