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.
There are so many deep learning libraries to choose from. Which are the good professional libraries that are worth learning and which are someones side project and should be avoided. It is hard to tell the difference. In this post you will discover the top deep learning libraries that you should consider learning and using in your own deep learning project. Popular Deep Learning Libraries Photo by Nikki, some rights reserved.
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.