Top 12 AI Tools, Libraries, and Platforms

With the rates of AI adoption growing rapidly, it's becoming more important than ever to know what options you have for AI tools, libraries, platforms, and the like — what they all can offer, what drawbacks they have, which one is best for your unique use case. In this article, we're making that decision process a little easier by listing the top 12 AI tools, libraries, and platforms, what they are typically used for, what pros and cons they come with, and more!

Note: These tools are listed in alphabetical order — no favoritism here!

1. Azure Machine Learning

If you don't have advanced programming skills but are looking to get into machine learning, you should check out Azure Machine Learning. (Note that you should have some knowledge of machine learning and data science to truly benefit from using this platform.) This cloud-based service provides tooling for deploying predictive models as analytic solutions. It can also be used to test machine learning models, run algorithms, and create recommender systems, to name a few. However, it's been criticized for its poor performance and unintuitive UI, particularly when it comes to writing code. Learn more about Azure Machine Learning here!

2. Caffe

Developed by Yangqin Jia, Caffe was created as part of Jia's Ph.D. research while at UC Berkeley. Now, it's an open-source framework for deep learning that supports various types of software architectures that were designed with image segmentation and image classification in mind. Caffe is touted for its easily readable source code and its quality of performance. Some negatives that have been pointed out are that you need to write new layers in Cude/C++ and it's difficult to use when writing protofiles for large networks. Learn more about Caffe here!

3. CNTK

CNTK (Computational Network Toolkit) is a deep learning toolkit developed by Microsoft that "describes neural networks as a series of computational steps via a directed graph." It can be used to help you easily combine different types of neural networks, has great performance, allows for distributed training, and is flexible. On the flip side, it has no easily readable source code and lacks in the visualization department. Learn more about CNTK here!

4. Deeplearning4j

DeepLearning4J calls itself an open-source, distributed deep learning library for the JVM. It's well-suited for training distributed deep learning networks and can process huge data without losing its pace. It can also integrate with Hadoop and Spark and can implement machine learning algorithms from scratch. On the other hand, Java is not a very popular language for machine learning, so DL4J can't rely on growing codebases as much as other libraries can, and development costs will likely be higher. Additionally, because it's built with Java, you must create explicit classes on your own in order to add matrices together, as opposed to with Python in which you don't have to create explicit classes. Learn more about DeepLearning4J here!

5. IBM Watson

IBM Watson is called a "question answering machine." It uses analytical powers and artificial intelligence to replicate human-like abilities to respond to questions optimally. It can help you make great business insights and make informed decisions based on thoroughly informed decisions. IBM also ensures that your data is protected with world-class security and encryption capabilities and that they will not share your data unless you say that they can. On the other hand, its disadvantages include that it is only available in English, it doesn't process structured data directly, and switching and integrating come with high costs. Learn more about IBM Watson here!

8. Keras

Keras is an open-source neural network library written in Python. If you're looking for something that will enable you to perform quick and easy experimentation, then you should check this one out! It works on convolutional neural networks and/or recurrent neural networks, and can also run on both CPU and GPU. Its pros are that it's easy to use and is pretty straightforward for devs who are familiar with deep learning, but its cons are that it can be frustrating to transcend surface-level customizations and that its data processing tools can be somewhat of a burden. Overall, though, this is an evolving API that's already come a long way — and there's no telling where it could go in the future! Learn more about Keras here!

7. Pybrain

PyBrain is an open-source, modular machine learning library. A completely Python-based framework, PyBrain aims to be a tool that can be used by developers ranging from students who are just beginning to explore the world of Python, as well as computer science researchers in the world of deep learning and neural networks. The PyBrains library is made up of algorithms that allow developers to work with concepts such as reinforcement learning, unsupervised machine learning, and neural networks. Learn more about PyBrain here!

8. Scikit-Learn

Scikit-learn is an open-source machine learning framework for Python that is useful for data mining, data analysis, and data visualization. It is good for classification, regression, clustering, dimensionality reduction, model selection, and preprocessing, to name a few. It is built on NumPy, SciPy, and matplotlib. Using Python, it works faster than R and has great performance. However, there is no distributed version available and it isn't ideal for larger datasets. Learn more about scikit-learn here!

9. Swift AI

Swift AI is a deep learning and neural network library for Swift, with support for Mac machines (and support for Linux coming soon). This library is made up of various tools that allow developers to create neural networks, create deep learning algorithms, and work with signal processing. On their GitHub page, they show example projects where Swift AI has been used to create software that can recognize patterns in human handwriting. Learn more about Swift AI here!

10. Tensorflow

Originally developed by members of Google's Machine Intelligence research division to conduct deep learning neural networks and machine learning research, TensorFlow is now a semi-open-source library that allows developers to perform numerical computations. AI developers can use the TensorFlow library to build and train neural networks in pattern recognition. It is written in Python and C++, two powerful and popular programming languages, and allows for distributed training. Some cons are that it doesn't contain many pre-trained models and there's no support for external datasets, like Caffe. Learn more about TensorFlow here!

11. Theano

Theano is a Python library for defining, optimizing, manipulating, and evaluating mathematical expressions using a computer algebra system. If you deal with deep learning, then you deal with lots of numerical tasks. Theano is very well-adapted to these types of tasks — especially matrix operations, symbolic variable and function definitions, and just-in-time compilation to CPU or GPU machine code. It's one of the oldest deep learning libraries, which means it is very well established but also means that it must often be used with other libraries if you want to have a high level of abstraction. Learn more about Theano here!

12. Torch

Torch is an open-source framework for scientific computing that supports machine learning algorithms. It is powered by LuaJIT, a scripting language that allows developers to code in C while interacting with Torch, and an underlying C/CUDA implementation. Some of Torche's prominent features, as listed on their website, are "a powerful N-dimensional array; linear algebra routines; neural network, and energy-based models; fast and efficient GPU support." Additionally, it can be ported to iOS and Android backends. Some drawbacks to Torch that have been pointed out include that fact that loading data from directories can prove difficult at times, and its reliance on Lua (a relatively new language) makes it less accessible. Learn more about Torch here!

What do you think about this list? Is there something else that we should have included? Let us know in the comments!