Coined first by Arthur Samuel in 1959, Machine Learning or ML is that part of AI that bestows machines the ability to learn and make them improve on their own.
With ML, developers can train machines to learn from their own experiences without explicitly programming to do the aforesaid. To accomplish so much using machine learning, we have a range of frameworks, tools (kit)s, modules, libraries, and so on. We’ll focus on ML libraries here.
Machine Learning Libraries
Typically, a ML library is a compilation of functions and routines readily available for use. A robust set of libraries is an indispensable part of a developer’s arsenal to research and write complex programs while saving themselves from writing a lot of code.
Libraries save developers from writing redundant code over and over. Also, there are all sorts of libraries to deal with different things. For example, we have text processing libraries, graphics libraries, data manipulation, and scientific computation.
As machine learning continues to give humanity new possibilities and woo newcomers, hundreds of ML libraries also have active development. Not all of them are great, though. The good news, however, is that several of them are.
Up ahead, we will discuss 15 of the best machine learning libraries that are preferred by machine learning enthusiasts and professionals around the globe.
P.S. - This article is strictly limited to explaining ML libraries ONLY! Hence, no modules and packages. For instance, statsmodels is an extremely efficient ML option for implementing statistical learning algorithms and time series modeling; however, it is a package and not a library.
1. Armadillo
Written in: C++
Since: N/A
Developer: NICTA research center, Australia and independent contributors
Used for: Linear algebra and scientific computing
Implemented using the C++ programming language, Armadillo is a linear algebra library employed for accomplishing the purposes of scientific computing. In addition to machine learning, Armadillo finds uses in:
- Bioinformatics,
- Computer vision,
- Econometrics,
- Pattern recognition,
- Signal processing, and
- Statistics.
Armadillo features a delayed-evaluation approach, which is achieved via template metaprogramming, for combining many operations into a single unified operation. This reduces or even eliminates the requirement of temporaries.
The Armadillo library offers a functionality resembling MATLAB and high-level syntax. The library is suitable for developing ML algorithms in C++. It also has the ability to implement research code into production-ready environments quickly.
Highlights
- Automatically uses OpenMP multithreading for speeding computation-intensive operations.
- Balanced ease-of-use and speed.
- Comes with an easy-to-use, straightforward interface.
- Provides support for:
- A subset of statistics and trigonometric functions,
- Complex numbers,
- Floating point numbers (single and double precision),
- Integers, and
- Sparse and dense matrices.
- Supports several matrix decompositions via integration with ARPACK, ATLAS, and LAPACK.
2. FANN
Written in: C
Since: November 2003
Developer: Steffen Nissen (original), several collaborators (present)
Used for: Developing multi-layer feed-forward artificial neural nets
FANN is an acronym for Fast Artificial Neural Network. As the name suggests, the open-source, machine learning library helps develop neural networks, multi-layer feed-forward artificial neural networks, to be specific.
Written in the C programming language, FANN provides support for both fully connected and sparsely connected neural nets. Since its advent in 2003, the machine learning library has been extensively used for research in:
- Aerospace engineering,
- AI,
- Biology,
- Environmental sciences,
- Genetics,
- Image recognition, and
- Machine learning.
FANN is an extremely easy-to-use library and comes with thorough, in-depth documentation. It is suitable for backpropagation training as well as evolving topology training.
Highlights
- Availability of a multitude of GUIs, such as:
- Agile Neural Network,
- FANNTool, and
- Neural View.
- Capable of using both fixed-point and floating-point numbers.
- Bindings available for 20+ programming languages and technologies, including:
- C#,
- Erlang,
- Go,
- Grass,
- Java,
- Lua,
- MATLAB,
- NodeJS,
- PHP,
- Python,
- R and
- Rust.
- Features the ability to store trained ANNs as .net files. This allows faster saving and loading of ANNs for future uses.
- Support available for cross-platform execution of single as well as multilayer networks.
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3. Keras
Written in: Python
Since: March 2015
Developer: François Chollet (original), various (present)
Used for: Deep learning
Keras is an open-source library that runs efficiently on CPU as well as GPU. It is used for deep learning, specifically for neural networks. The popular ML library works with the building blocks of neural networks, such as:
- Activation functions,
- Layers,
- Objectives, and
- Optimizers.
Other than the standard neural nets, Keras also provides support for convolutional and recurrent neural networks. The ML library also packs a plethora of features for working with images and text images.
Highlights
- Can run on top of:
- Microsoft Cognitive Toolkit (CNTK),
- PlaidML,
- R,
- TensorFlow, and
- Theano.
- Enables fast experimentation with deep neural networks.
- It offers a high-level, intuitive set of abstractions for easing the development of deep learning models.
- Superb community support.
- Support available in TensorFlow’s core library.
4. Matplotlib
Written in: Python
Since: 2003
Developer: John D. Hunter (original), Michael Droettboom, et al. (present)
Used for: Data visualization and plotting
Matplotlib is a ML library employed for producing publication-ready figures, images, and plots in a range of formats via 2D plotting. Using only a few lines of code, the Matplotlib library allows generating detailed, high-quality:
- Bar charts,
- Error charts,
- Histograms,
- Scatter plots, etc.
Although Matplotlib is fairly user-friendly, users accustomed to the MATLAB interface will find it even easier to get on board, especially using the pyplot module. The ml library offers an object-oriented API using standard GUI toolkits like GTK+, Qt, and wxPython to embed graphs and plots in applications.
Highlights
- Ample documentation.
- Excellent community support.
- Functionality extension using many toolkits, including:
- Cartopy,
- Excel tools,
- GTK tools, and
- Qt interface.
- The greater degree of customization.
- SciPy uses Matplotlib.
5. mlpack
Written in: C++
Since: February 2008
Developer: Georgia Institute of Technology and the mlpack community
Used as: Software library
Built on top of the popular linear algebra library Armadillo, mlpack is an ML library that emphasizes ease-of-use, scalability, and speed. The primary intent of the mplack library is to offer an extensible, fast, and flexible way of implementing ML algorithms.
Although meant for C++, bindings for mlpack is available for Go, Julia, Python, and R programming languages. It also features simple command-line programs and C++ classes that can be integrated into large-scale ML solutions.
Highlights
- Apt for beginners.
- High-quality documentation.
- Maximizes flexibility and performance for advanced users by exploiting C++ features.
- Provides support for a wide range of ML algorithms and models, including:
- Collaborative filtering,
- Density estimation trees,
- Euclidean minimum spanning trees,
- Gaussian Mixture Models (GMMs),
- K-Means clustering,
- Logistic regression,
- Naive Bayes classifier,
- Sparse coding and Sparse dictionary learning and
- Tree-based range search.
- Supports recurrent neural networks by offering template classes for GRU and LSTM structures.
- The binding system is extensible to other programming languages.
6. NLTK
Written in: Python
Since: 2001
Developer: Steven Bird, Edward Loper, and Ewan Klein (original), Team NLTK (present)
Used for: Text processing
NLTK stands for Natural Language Toolkit. As the name suggests, it is a Python library intended for NLP tasks, like language modeling, named entity recognition, and neural machine translation. The machine learning library fulfills one and all text processing needs, including:
- Chunking,
- Dependency parsing,
- Lemmatization,
- Stemming, and
- Word tokenization.
Interestingly, NLTK is not just a single ML library, but, instead, a collection of libraries (and programs).
Highlights
- Comes with a book detailing the underlying concepts and a cookbook.
- Excellent for education and research.
- N-gram and collocations are available.
- Offers a synonym bank dubbed wordnet.
- Supports named-entity recognition.
7. NumPy
Written in: Python
Since: 2006
Developer: Travis Oliphant (original), NumPy community (present)
Used for: Scientific computation
NumPy is a contraction for numerical Python. That name clearly suggests that it is a library intended for computation. Using the Python-based library allows developers to save a lot of time in scientific computations that involve heavy matrix operations.
The NumPy library leverages a special class of arrays, dubbed Numpy arrays, that perform vast matrix-based calculations in mere milliseconds. This is made possible due to the implementation of the Numpy arrays in the C programming language.
Due to the aforementioned, NumPy has become one of the most beloved libraries/packages for machine learning, especially natural language processing.
Highlights
- Ability to serve as an efficient multi-dimensional container for any generic data of any data type.
- Features an exhaustive set of high complexity mathematical functions for processing huge multi-dimensional arrays and matrices.
- Ideal for handling Fourier transforms linear algebra and random numbers.
- Superb community support.
- Used by TensorFlow for manipulating tensors at the backend.
- Out-of-the-box tools for integrating C, C++, and Fortran code.
8. OpenNN
Written in: C++
Since: 2003
Developer: International Center for Numerical Methods in Engineering (original), Artelnics (present)
Used for: Advanced analytics and neural networks implementation
OpenNN is an open-source machine learning library that leverages ML techniques for solving data mining and predictive analytics problems across various fields. The library has been employed for dealing with problems in chemistry, energy, and engineering.
The primary advantage of using OpenNN is its high-performance. This is attributed to the library being developed in the C++ programming language. The ML library features sophisticated algorithms and utilities to accomplish classification, forecasting, regression, et cetera.
Highlights
- Capable of implementing any number of layers of non-linear processing units for supervised learning.
- Enables multiprocessing programming using OpenMP.
- It features data mining algorithms as a bundle of functions integrated into other software tools through an API.
- More than just a library, a general-purpose AI software package.
9. Pandas
Written in: C, Cython, and Python
Since: January 2008
Developer: Wes McKinney (original), pandas community (present)
Used for: Managing tabular data
pandas are the go-to machine learning library when it comes to dealing with gargantuan proportions of tabular data. Pandas is to Python what Microsoft Excel is to Windows. The ML library cuts the effort required for big, complex calculations to a mere few code lines.
Also, pandas feature a long list of pre-existing commands that will save ML developers from adding code for various mathematical operations. Aside from data manipulation, the Pandas library also helps in transforming and visualizing the same. The pandas library leverages two main types of data structures:
- Series (1-dimensional), and
- DataFrame (2-dimensional).
Using this duo in-line allows developers to handle a wide range of data requirements and scenarios belonging to engineering, finance, science, statistics, etc.
Data scientists rely on pandas to cut the clutter of writing boilerplate code and focus more on the actual problem-solving associated with the tabular data at hand.
Highlights
- Capable to efficiently handle:
- Any form of statistical or observational datasets.
- Arbitrary matrix data with homogeneous or heterogeneous data.
- Ordered and unordered time-series data.
- Tabular data with columns of heterogeneous data.
- Excellent community support.
- Exceptional performance in handling uneven time-series data.
- Library of choice for solving real-world data analysis in Python.
- Supports expressive, fast, and flexible data structures that can work with both labeled and relational data.
10. PyTorch
Written in: C++, CUDA, and Python
Since: September 2016
Developer: Adam Paszke, Sam Gross, Soumith Chintala, and Gregory (original), FAIR [Facebook’s AI Research lab] (present)
Used for: Deep learning
Torch, now defunct, is a deep learning library for the Lua programming language. Facebook took it and built it into a library that has become one of the leading Python machine learning libraries, PyTorch. Here Py represents Python.
PyTorch isn’t as popular as TensorFlow but gains the upper hand over the latter with the execution of Dynamic Graphs. When researching, especially while working with low-level APIs, the ability to model components on the fly is desirable. The ML library allows doing so.
Compared to other popular machine learning libraries, PyTorch has a tender learning curve. Hence, it is a suitable option for machine learning and data science beginners. Additionally, the library offers a range of tools for computer vision, machine learning, and NLP.
Highlights
- Beginner-friendly.
- It can perform computations on tensors.
- Custom data loaders.
- Developed by Facebook.
- Good community support.
- Multi-GPU support.
- A robust framework for developing computational graphs on the go and change the same during runtime.
- Simplified preprocessors.
- Smoother integration with the Python data science stack.
11. Scikit-Learn
Written in: C, C++, Cython, and Python
Since: June 2007
Developer: David Cournapeau (original), Inria
Used for: Data preprocessing and modeling
Whether it’s decision trees, linear regression, logistics regression, or SVMs, you name it, and Scikit-Learn will have it. It is one of the most popular machine learning libraries for building machine learning algorithms. Scikit-Learn also flaunts the ability to:
- Preprocess data, and
- Vectorize text using BOW, hashing vectorization, TF-IDF, etc.
Written in C and Python, Scikit-Learn enjoys a growing, loyal community that includes programmers, machine learning hobbyists, and IT professionals worldwide. It is built on top of NumPy and SciPy, two of the most popular ML libraries for scientific computation.
The only issue with the Scikit-learn library is that it doesn’t offer good support for distributed computing aimed at large scale production environment apps. Hopefully, it will get fixed in the upcoming releases of the popular ML library.
Highlights
- It could also be used for data analysis and data mining.
- Features a wide range of supervised and unsupervised learning algorithms.
- Mushrooming community support.
- Other than model handling and preprocessing, the ML library can also handle functions belonging to:
- Classification,
- Clustering,
- Dimensionality reduction, and
- Regression.
12. SciPy
Written in: C, C++, Fortran, and Python
Since: 2001
Developer: Travis Oliphant, Pearu Peterson, and Eric Jones (original), SciPy community (present)
Used for: Scientific computation and technical computing
In 2001, three data scientists and engineers, namely Travis Oliphant, Eric Jones, and Pearu Peterson, merged several useful Python libraries for analytics and scientific computing into a single unified and standardized library. It was dubbed SciPy.
At present, SciPy is one of the leading machine libraries for accomplishing scientific computation. It leverages NumPy arrays, which are multi-dimensional arrays, offered by the NumPy module. Other than the NumPy module, SciPy features separate modules for accomplishing:
- Fast Fourier transform,
- Image optimization,
- Integration interpolation,
- Linear algebra,
- ODE (Ordinary Differential Equation) solving,
- Signal and image processing,
- Special functions, etc.
In addition to working with the NumPy arrays, SciPy is designed to offer efficient, user-intuitive numerical functions. The library relies on the NumPy module for array manipulation subroutines.
Highlights
- Also, a family of conferences for the developers and tools in Europe, the United States, and India.
- Mushrooming community.
- It offers a wide range of sub-packages, such as cluster, fft, interpolates, and ndimage.
- Part of the NumPy stack.
13. Shogun
Written in: C++
Since: 1999
Developer: Gunnar Rätsch and Soeren Sonnenburg (original), Soeren Sonnenburg, Sergey Lisitsyn, Heiko Strathmann, Fernando Iglesias, and Viktor Gal (present)
Used for: Software library
Shogun is a free and open-source machine learning library that offers a wide range of machine learning algorithms and data structures. Unlike other popular ML libraries, Shogun focuses on kernel machines for classification and regression problems. The ML library provides support for:
- Clustering algorithms: k-means and GMM,
- Dimensionality reduction algorithms,
- K-Nearest Neighbors,
- Kernel Perceptrons,
- Linear discriminant analysis, etc.
Implemented in C++, Shogun offers a single platform for combining several algorithm classes, data representations, and general-purpose tools for quick prototyping of data pipelines. The library flaunts a reliable community that includes professionals from around the globe.
Highlights
- Fast prototyping and flexible embedding in workflows.
- Features a full implementation of Hidden Markov Models.
- Interfaces available (using SWIG) for:
- C#,
- Java,
- Lua,
- Octave,
- Python,
- R and
- Ruby.
- Suitable for educational and research purposes.
- Underactive development since 1999.
14. TensorFlow
Written in: C++, CUDA, and Python
Since: November 2015
Developer: Google Brain Team
Used for: Deep learning
TensorFlow is among the best libraries available for accomplishing deep learning. Developed by Google, the ML library is a get-it-started-instantly option for product-based firms as it offers excellent model prototyping, production, and everything in between.
The TensorFlow library features a web-based visualization tool called the Tensorboard that allows devs to visualize model parameters, gradients, and performance. The DL library offers frameworks like TensorFlow Lite and TensorFlow Serving to deploy ML models readily.
Despite all its perks, the machine learning library is criticized for its lousy implementation of graphs. This is because the library demands compiling the graph first. Hopefully, we will witness it get better at the aforesaid with the future rollouts.
Highlights
- Backed by Google.
- Exposes highly stable C++ and Python APIs. It can also expose backward compatible APIs for other programming languages. (These, however, can be unstable.)
- Extensive documentation.
- Flaunts a flexible architecture that allows running on a wide range of CPUs, GPUs, and TPUs (Tensor Processing Units).
- More than just a library, a popular computational framework for developing robust machine learning models.
- Provides support for a good range of toolkits for developing ML models at various levels of abstraction.
- Reliable, giant community.
15. Theano
Written in: CUDA, Python
Since: 2007
Developer: MILA (Montreal Institute for Learning Algorithms), University of Montreal
Used for: Scientific computing
Built on top of NumPy, Theano is one of the speediest machine learning libraries. It offers tight integration with NumPy and an interface very much similar to the aforementioned. Theano works as an optimizing compiler for evaluating and manipulating:
- Mathematical expressions, and
- Matrix calculations.
Although Theano can work on both the CPU and GPU architectures, working on the latter yields speedier results. The GPU's machine learning library can be as much as 140 times faster while on a CPU when performing data-intensive computations.
Highlights
- Avoids bugs and errors automatically while working with exponential and logarithmic functions.
- Efficient symbolic differentiation.
- Evaluates expressions faster with dynamic C code generation.
- Offers inbuilt tools for unit testing and validation.
- Speedier execution.
Other Honorable Mentions
As already told, there are hundreds of thousands of machine learning libraries. That means that the entries on the list we’ve presented aren’t the only best ones. Explaining all of them, however, goes beyond the scope of this write-up.
Hence, this section is dedicated to briefly cover some more great machine learning libraries available out there. Here’s the list:
- DyNet - A neural network library, DyNet builds its computational graph on the go. This simplifies as well as enhances the implementation of variable-input and variable-output models.
DyNet is designed to work great with networks having dynamic structures that change with every training instance. Although written in C++, bindings for the ML library are available for Python. - jblas - jblas is a cross-platform linear algebra library for the Java programming language. It is built on top of BLAS and LAPACK. Ever since its release in March 2009, the ML library is gaining traction for accomplishing scientific computing.
jblas features precompiled binaries and are designed to be used with native code via the JNI (Java Native Interface). It is typically used as part of software packages like JLabGroovy and UJMP (Universal Java Matrix Library). - NetworkX - Licensed under the BSD-new license, NetworkX is a Python library developed for studying graphs and networks. It is designed to operate in the real-world i.e., large graphs.
NetworkX depends on a pure-Python “dictionary of dictionary” data structure. This makes the machine learning library a highly efficient, portable, and scalable option for analyzing networks and social networks. - SHARK - Written in C++, SHARK, is a fast and modular machine learning library. It offers several ML techniques, most notably kernel-based learning algorithms, linear and non-linear optimization, and neural networks. SHARK is not only an excellent ML library for research purposes but also a powerful toolbox for building real-world ML-based applications.
Conclusion
This is now the end of the 15 best machine learning libraries article. No matter the programming language or the area a developer is working in, learning to work with libraries is important. Doing so helps in decomplexing the things and to cut the tedious effort.
Libraries come and go; however, the knowledge stays. Once you become well-acquainted with libraries' underlying concepts, you can easily switch out or expand to other available options. All the best!
Do you agree with our list? What libraries should or shouldn’t be on the list? Let us know via comments. Want to become better at machine learning? Try these best machine learning tutorials.
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