But where do you start? Which library do you use? There are just so many! This list is by no means exhaustive, it's simply a list of libraries that I've used in my computer vision career and found particular useful at one time or another. Some of these libraries I use more than others -- specifically, Keras, mxnet, and sklearn-theano.
Element AI's BAyesian Active Learning library (BaaL library) is now open source and available on GitHub. In this article, we briefly describe active learning, its potential use with deep networks and the specific capabilities of our BaaL library. Machine learning applications generally require a huge amount of data, and in many cases, this data cannot be easily acquired. What's more, even when data is readily available, it often is not possible to label it efficiently. Active learning aims at reducing the amount of labelled data needed to train machine learning models.
We are very excited today to open-source Sony's neural network libraries, a software that helps the workflows of deep learning research, development and production. Neural networks are the core ingredients of deep learning models. Deep learning has first received huge attention in 2012, when an image classification model accomplished a great leap in image recognition, winning against other models with a large gap, in the ImageNet Large Scale Visual Recognition Challenge. Nowadays, deep learning is widely used in many applications as an essential tool, not only as a pattern recognition algorithm, but also as a tool capable of modeling black-box systems. The architectures of deep learning models vary at a wide range, in various aspects; from small to large, from feed-forward to recurrent, from unsupervised to supervised and so on.
Michael Li is founder and CEO at The Data Incubator. The company offers curriculum based on feedback from corporate and government partners about the technologies they are using and learning, for masters and PhDs. Below is a ranking of 23 open-source deep learning libraries that are useful for Data Science, based on Github and Stack Overflow activity, as well as Google search results. The table shows standardized scores, where a value of 1 means one standard deviation above average (average score of 0). For example, Caffe is one standard deviation above average in Github activity, while deeplearning4j is close to average.
Why is it so hard to install deep Learning / Neural Network libraries? I switched to Linux because a lot of different sources indicate that a installation on windows/osx is even harder. First I tried to install Caffe. But after a while I had to give up. After that I convinced myself that I could live without c and tried a python environment.