There are numerous libraries and frameworks used for building machine and deep learning models; about 50 of them are mature enough to be appreciated by developers. Are you wondering which one will be the most suitable for your project? This is a common concern, so we decided to create a comparison of Top 10 machine learning libraries based on our experience. Read on!
What are the best programming languages for machine learning?
Before we jump into the world of libraries and frameworks, there is one important question that should be clarified: which programming languages work best for machine learning algorithms? Why exactly you should know this? There are a few reasons, depending on your situation:
- you are a developer that wants to know whether it’s worth to invest time in improving your skills in the particular language,
- you are wondering on what to focus when recruiting developers for your project or looking for a service provider,
- you need to stand up to the challenge of selecting the right technologies for the project you are working on.
The decisive factor that makes one programming language more suitable for machine learning algorithms than other it’s the number of libraries available for this specific language, as well as their usefulness and the advantages they bring the developers. Therefore, the programming languages used most commonly in machine learning are the following:
- Python. It’s not a secret that Python is the absolute leader of the most popular libraries count.
- R, that is well- known as a language for statisticians, and this is one of the reasons why it does have an impressive amount of solutions for machine learning.
- Java. Even though Java may not be the leader, but still the developers can take advantage of some amazing libraries for Java.
- Scala. The widely used cluster-computing framework Spart is developed in Scala and is currently the first choice for big data analysis.
This is the absolute Top 5 based on the number of mature machine learning and deep learning libraries available for these languages, as well as their level of popularity among machine learning developers.
Top 10 machine learning libraries: a comparison
So which machine learning libraries are the best? Let’s dive into Codete’s comparison of the best options available right now for developers and data scientists!
It’s the absolute leader of the race, the most used and the fastest growing (at least according to measurable indicators such as job listings number and Google Trends) machine learning framework nowadays. Tensorflow was created by Google and it’s originally written in Python, but is now available in almost all popular programming languages, which is a huge advantage.
When talking about TensorFlow, you can’t help but mention Keras too. It’s user-friendly and intuitive high-level neural network library written in Python, that you can use to build and train state-of-the-art machine learning models.
One of the advantages of Keras is that you can run it on top of TensorFlow or other libraries such as Theano or Microsoft Cognitive Toolkit. It allows building networks using predefined components like layers or sub-networks. Because of its flexibility, wide range of features, ease of use (in comparison with TensorFlow, for instance) and growing community, it may take the lead soon.
Scikit-learn is another well-known open-source machine learning library for Python, built on NumPy, SciPy, and matplotlib, so it’s well-integrated with the whole SciPy stack. Scikit-learn supports an impressive number of common machine learning algorithms and models that can be used for clustering, regression, classification, etc.
PyTorch is an open-source library created by another technology giant – Facebook, written mainly in Python and based on the Torch library. Interestingly, there is also a C++ version of the frontend available. PyTorch provides two high-level features: tensor computation with strong GPU acceleration, as well as deep neural networks created on a tape-based autograd system.
More than just a library, H2O can be described as a whole machine learning and AI platform. It’s available in open source and enterprise versions, supports the most popular statistical and machine learning algorithms, and what is more, it even enables the automation of machine learning workflows thanks to AutoML feature. It’s worth noting that the platform allows users to build models without coding, because it provides an interactive, graphical user interface. If you prefer to stick to coding, you can still use R or Python.
Caffe is a deep learning framework that is widely used for computer vision or speech recognition and is known for its efficiency and velocity. According to its creators, among the advantages of Caffe are: expressive architecture, extensible code that fosters active development, speed of processing (for instance, over 60M images per day with a single NVIDIA K40 GPU), and engaged community.
With this Spark’s machine learning library developing models becomes scalable and painless. It provides common algorithms, such as classification, regression, clustering, and collaborative filtering. MLlib can run on Hadoop, Mesos, Kubernetes, standalone, or in the cloud: literally everywhere! One of the biggest pros of this library is that it’s intended to run on Big Data. This means that it allows training the models on huge volumes of data, which you would find quite hard using other libraries.
As you might already have a feeling, the ‘r’ in mlr stands for R. Indeed, mlr is a framework that makes the R developers’ lives much easier, as it provides supervised (classification, regression, and even survival analysis) and unsupervised (clustering) machine learning methods. It can be scaled with no effort and it’s integrated with OpenML online platform, which means that online collaboration is as easy as pie.
Now it’s time for a deep learning library written for Java. Abbreviated DL4J, it is compatible with Java Virtual Machine languages (Scala, Clojure, Kotlin…). It uses distributed computing frameworks such as Apache Spark or Hadoop, and in terms of performance, it doesn’t fall behind Caffe. Naturally, it’s an open-source project with detailed documentation.
If you have arrived at the end of the article, you are surely looking for the answer: which machine learning library is the best? Well, actually there is no simple answer. It depends on your project, your programming language, what exactly you want to achieve and which features you value the most.
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