The Top 8 AI Frameworks and Machine Learning Libraries
As machine learning gains a stronger foothold in business, companies are investing more resources into AI-powered tools that can automate perfunctory tasks and mine for deep insights into data. While business users may only see the output of machine learning and AI, technical users like data scientists have a much larger stake in the back-end workings of these tools.
One of the most important assets for data scientists is access to AI libraries and tools that can help them acquire data, train models, refine their results, and more.
Table of Contents
We’ve identified the top AI frameworks and machine learning libraries based on our experience in augmented analytics (the combination of machine learning and natural language generation).
That said, every data scientist has different needs when it comes to bringing their projects to life. Use this list as a jumping-off point for your research into the machine learning library that will work best for you.
“An open source machine learning framework for everyone”
TensorFlow is Google’s open source AI framework for machine learning and high performance numerical computation.
TensorFlow is a Python library that invokes C++ to construct and execute dataflow graphs. It supports many classification and regression algorithms, and more generally, deep learning and neural networks.
One of the more popular AI libraries, TensorFlow services clients like AirBnB, eBay, Dropbox, and Coca-Cola.
Plus, being backed by Google has its perks. TensorFlow can be learned and used on Colaboratory, a Jupyter notebook environment that runs in the cloud, requires no set-up, and is designed to democratize machine learning education and research.
Some of TensorFlow’s biggest benefits are its simplifications and abstractions, which keeps code lean and development efficient.
“Machine learning in Python”
Scikit-learn is an open source, commercially usable AI library. Another Python library, scikit-learn supports both supervised and unsupervised machine learning. Specifically, it supports classification, regression, and clustering algorithms, as well as dimensionality reduction, model selection, and preprocessing.
It’s built on the NumPY, matplotlib, and SciPy libraries, and in fact, the name “scikit-learn” is a play on “SciPy Toolkit.”
Scikit-learn markets itself as “simple and efficient tools for data mining and data analysis” that is “accessible to everybody, and reusable in various contexts.”
To support these claims, scikit-learn offers an extensive user guide so that data scientists can quickly access resources on anything from multiclass and multilabel algorithms to covariance estimation.
“A Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently”
Theano is a Python library and optimizing compiler designed for manipulating and evaluating expressions. In particular, Theano evaluates matrix-valued expressions.
Speed is one of Theano’s strongest suits. It can compete toe-to-toe with the speed of hand-crafted C language implementations that involve a lot of data. By taking advantage of recent GPUs, Theano has also been able to top C on a CPU by a significant degree.
By pairing elements of a computer algebra system (CAS) with elements of an optimizing compiler, Theano provides an ideal environment for tasks where complicated mathematical expressions require repeated, fast evaluation. It can minimize extraneous compilation and analysis while providing important symbolic features.
Even though new development has ceased for Theano, it’s still a powerful and efficient platform for deep learning.
“Deep learning framework”
Caffe is an open deep learning framework developed by Berkeley AI Research in collaboration with community contributors, and it offers both models and worked examples for deep learning.
Caffe prioritizes expression, speed, and modularity in its framework. In fact, its architecture supports configuration-defined models and optimization without hard coding, as well as the ability to switch between CPU and GPU.
Plus, Caffe is highly adaptive to research experiments and industry deployments because it can process over 60M images per day with a single NVIDIA K40 GPU— one of the fastest convnet implementations available, according to Caffe.
Caffe’s language is C++ and CUDA with Command line, Python, and MATLAB interfaces. Caffe’s Berkeley Vision and Learning Center models are licensed for unrestricted use, and their Model Zoo offers an open collection of deep models designed to share innovation and research.
“The Python Deep Learning library”
Keras is a high-level neural network API that can run on top of TensorFlow, Microsoft Cognitive Toolkit, or Theano. This Python deep learning library facilitates fast experimentation and claims that “being able to go from idea to result with the least possible delay is key to doing good research.”
Instead of an end-to-end machine learning framework, Keras operates as a user-friendly, easily extensible interface that supports modularity and total expressiveness. Standalone modules — such as neural layers, cost functions, and more — can be combined with few restrictions, and new modules are easy to add.
With consistent and simple APIs, user actions are minimized for common use cases. It can run in both CPU and GPU as well.
6. Microsoft Cognitive Toolkit
“A free, easy-to-use, open-source, commercial-grade toolkit that trains deep learning algorithms to learn like the human brain.”
Previously known as Microsoft CNTK, Microsoft Cognitive Toolkit is an open source deep learning library designed to support robust, commercial-grade datasets and algorithms.
With big-name clients like Skype, Cortana, and Bing, Microsoft Cognitive Toolkit offers efficient scalability from a single CPU to GPUs to multiple machines— without sacrificing a quality degree of speed and accuracy.
Microsoft Cognitive Toolkit supports C++, Python, C#, and BrainScript. It offers pre-built algorithms for training, all of which can be customized, though you can use always use your own. Customization opportunities extend to parameters, algorithms, and networks.
“An open source deep learning platform that provides a seamless path from research prototyping to production deployment.”
PyTorch is an open source machine learning library for Python that was developed mainly by Facebook’s AI research group.
PyTorch supports both CPU and GPU computations and offers scalable distributed training and performance optimization in research and production. It’s two high-level features include tensor computation (similar to NumPy) with GPU acceleration and deep neural networks built on a tape-based autodiff system.
With extensive tools and libraries, PyTorch provides plenty of resources to support development, including:
- AllenNLP, an open source research library designed to evaluate deep learning models for natural language processing.
- ELF, a game research platform that allows developers to train and test algorithms in different game environments.
- Glow, a machine learning compiler that enhances performance for deep learning frameworks on various hardware platforms.
“A scientific computing framework for LuaJIT”
Similar to PyTorch, Torch is a Tensor library that’s similar to NumPy and also supports GPU (in fact, Torch proclaims that they put GPUs “first”). Unlike PyTorch, Torch is wrapped in LuaJIT, with an underlying C/CUDA implementation.
A scientific computing framework, Torch prioritizes speed, flexibility, and simplicity when it comes to building algorithms.
With popular neural networks and optimization libraries, Torch provides users with libraries that are easy to use while enabling flexible implementation of complex neural network topologies.
AI Libraries and AnswerRocket
Why are we so invested in AI frameworks and machine learning libraries?
AnswerRocket can integrate with any open AI platform.
Our software speaks both Python and R. And if you want to see exactly how our data analysis works, you can easily open our algorithms in Jupyter notebook. Data scientists have the ability — and opportunity — to write extensions for our software to suit the needs of your business.
AnswerRocket’s streamlined platform shows when frameworks like TensorFlow are invoked so that data scientists can validate responses and make changes directly in the framework of their choosing.
We think it’s important for machine learning platforms to be easy to use for the business person who needs results, while still packing a huge punch for the technical teams who want to push the boundaries of data analysis with customizable extensions. Choosing the right AI framework or machine learning library is key to facilitating success.
See How AI Powers AnswerRocket
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