Top 5 machine learning frameworks for Java and Python
Machine learning’s explosive growth has been fueled by a number of open source tools making it easier for developers to learn its techniques. We take a look at five of our favorite machine learning frameworks for Java and Python.
According to our tech experts, the future’s looking bright for artificial intelligence and machine learning. So if you’re looking to learn one of the most desirable skills in tech, you’ve come to the right place. We’ve already gone over the top machine learning libraries and open source projects, so now we’re taking a close look at frameworks.
In no particular order:
Developed by a team at the National University of Singapore, Apache Singa is a flexible and scalable deep learning platform for big data analytics. This deep learning framework provides a flexible architecture for scalable distributed training on large volumes of data. Singa is extensible to run over a wide range of hardware. The main applications are in image recognition and natural language processing (NLP).
Singa, currently an Apache Incubator project, provides a simple programming model that can work across a cluster of nodes. The distributed deep learning uses model partitioning and parallelizing during the training process. In general, Singa supports traditional machine learning models like logistic regression.
Another open source offering from Apache, Apache Mahout is a distributed linear algebra framework for creating scalable performant machine learning applications. Designed to let mathematicians, statisticians, and data scientists quickly implement their own algorithms, Mahout focuses primarily on collaborative filtering, clustering, and classification.
Apache Mahout gives you the ability to “roll-your-own math in an interactive environment that actually executes on a big data platform, then moves exactly the same code into your app and deploy. Mahout Samsara provides a distributed linear algebra and stats engine that is performant and distributed along with an interactive shell (now inside Apache Zeppelin) as well as the library to link into your application in production.” Mahout often piggybacks on top of the Apache Hadoop platform using the map/reduce paradigm, but this does not restrict contributions to other Hadoop-based implementations.
Apache Mahout is written in Java and Scala. You can try it here.
Microsoft’s Cognitive Toolkit is an open source deep-learning toolkit for training algorithms to learn like the human brain. CNTK makes it easy for users to utilize popular machine learning models like feed-forwards DNNs, convolutional neural networks, and recurrent neural networks.
This tool is unquestionably meant to use neural networks to go through large datasets of unstructured data. With faster training times and easy to use architecture, CNTK is highly customizable, allowing you to choose your own parameters, algorithms, and networks. Thanks to its support of “multi-machine-multi-GPU” backends, CNTK easily outperforms many of its competitors. Microsoft even offers an introductory video, if you’re interested.
Microsoft CNTK is written in in Python and C++. You can try it here.
Developed by the Berkley AI Research team, Caffe is a deep learning framework made for expression, speed, and modularity. The expressive architecture encourages application and custom innovation. Configuration options allow users to switch between CPU and GPU by setting a single flag. Caffe’s extensible code has helped fuel its early growth, making it another highly starred GitHub machine learning project.
Caffe’s speed makes it valuable for research institutions and industry deployments. It was developed for computer vision/image classification by leveraging Convolutional Neural Networks(CNNs). Caffe offers the Model Zoo, which is a set of pre-trained models that don’t require any coding to implement. However, it should be pointed out that Caffe is best suited for building applications and not intended for anything other than computer-vision.
Caffe is written in C++ with a Python interface. You can get it here
Last but never least, our favorite machine learning framework is the incomparable TensorFlow. TensorFlow is an open source software library for numerical computation using data flow graphs. TensorFlow is the most-forked machine learning project on GitHub and boasts some of the highest participation by contributors as well.
TensorFlow’s flexible architecture makes it easy for users to deploy computation to one or more CPUs or GPUs with a single API, no matter whether to a desktop, server, or even a mobile phone. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them.
TensorFlow is written mostly in Python, but it also supports some usage with Java and Go. Read our review of TensorFlow 1.4 here.
Python’s continued relevance into 2018 certainly owes something to the explosion of machine learning in the last few years. Some of the world’s most popular ML frameworks and libraries are written in or primarily supported by Python, including TensorFlow, Keras, Theano, as well as smaller projects like sci-kit learn, Chainer, H20, Microsoft Azure Studio, Veles, and Neon. Whatever’s not in Python is also supported with C++, like Microsoft CNTK and Caffe. (Torch, another honorable mention, is written in Lua with support for C++.)
So, if you’re interested in picking up some ML skills to wow employers or catch the latest wave in tech, it might be time to dust off your old Python or C++ textbooks and get going.