We have already written a few articles about Pylearn2. Today we’ll look at PyBrain. It is another Python neural networks library, and this is where similiarites end.
We’d like to be able to predict stock market. That seems like a nice way of making money. We’ll address the fundamental issue: can stocks be predicted in the short term, that is a few days ahead?
Out of 215 contestants, we placed 8th in the Cats and Dogs competition at Kaggle. The top ten finish gave us the master badge. The competition was about discerning the animals in images and here’s how we did it.
IPython is known for the notebooks. But the first thing they list on their homepage is a “powerful interactive shell”. And that’s true - if you use Python interactively, you’ll dig IPython.
A while ago we’ve shown how to get predictions from a Pylearn2 model. It is a little tricky, partly because of splitting data into batches. If you’re able to fit your data in memory, you can strip the batch handling code and it becomes easier to see what’s going on. We exercise the concept to distinguish cats from dogs again, with superior results.
Recently at least two research teams made their pre-trained deep convolutional networks available, so you can classify your images right away. We’ll see how to go about it, with data from the Cats & Dogs competition at Kaggle as an example.
We continue with CIFAR-10-based competition at Kaggle to get to know DropConnect. It’s supposed to be an improvement over dropout. And dropout is certainly one of the bigger steps forward in neural network development. Is DropConnect really better than dropout?
A/B testing is a way to optimize a web page. Half of visitors see one version, the other half another, so you can tell which version is more conducive to your goal - for example selling something. Since June 2013 A/B testing can be conveniently done with Google Analytics. Here’s how.
Recently Rob Zinkov published his selection of interesting-looking NIPS papers. Inspired by this, we list some more. Rob seems to like Bayesian stuff, we’re more into neural networks. If you feel like browsing, Andrej Karpathy has a page with all NIPS 2013 papers. They are categorized by topics discovered by running LDA. When you see an interesting paper, you can discover ones ranked similiar by TF-IDF. Here’s what we found.
Object recognition in images is where deep learning, and specifically convolutional neural networks, are often applied and benchmarked these days. To get a piece of the action, we’ll be using Alex Krizhevsky’s cuda-convnet, a shining diamond of machine learning software, in a Kaggle competition.