Machine learning is that part of AI which specifically deals with the learning ability of machines. Deep learning is a subset of ML that tries to emulate the working of a human brain using artificial neural networks (ANNs). This article details the best machine learning libraries.
What is a Machine Learning Library?
The concept of libraries is akin to programming. It is a collection of functions and methods pertaining to some specific task. As such, a machine learning library is a collection of functions and routines that are related to performing ML operations.
Libraries are integral to modern-day programming as they save developers from reinventing the wheel while also cutting a lot of effort and time.
5 Best Machine Learning Libraries
Developer – Travis Oliphant and the NumPy Community
Since – 2006
Written using – Python
Purpose – Scientific computation
The Python-based library cuts a lot of time and effort for developers in scientific computation, especially heavy matrix operations.
- Has a vast number of high complexity mathematical functions to process big multi-dimensional arrays and matrices.
- Plug-and-play tools to integrate C, C++, and Fortran code.
- Appropriate to handle Fourier transforms and random numbers.
NumPy uses Numpy arrays, a special type of arrays implemented in C that have the capacity to perform enormous matrix-based calculations in just a few milliseconds. It is an extremely popular Python ML library for natural language processing.
Developer – Wes McKinney and the pandas Community
Written using – C, Cython, and Python
Since – 2008
Purpose – Data manipulation and analysis
When you’re dealing with mammoth proportions of tabular data, there are few powerful and the best ML libraries like pandas, that you can rely on. It helps to perform complex calculations with a few lines of code.
- Less focus on writing boilerplate code and more focus on actual problem-solving.
- Excellent handling of uneven time-series data.
- Has flexible data structures that work well with relational as well as labeled data.
Other than data manipulation, pandas also let developers confidently deal with transforming and visualizing data. The popular ML library has two major types of data structures; Series (1-dimensional) and DataFrame (2-dimensional).
Developer – Georgia Institute of Technology and the mlpack Community
Written using – C++
Since – 2008
Purpose – Linear algebra
mlpack offers an efficient way to deal with linear algebra. It is built on top of the popular Armadillo ML library and aims to offer a flexible and fast way to implement ML algorithms. Also, it is a highly popular C++ machine learning library.
- Maximizes flexibility and performance.
- Offers support for an array of ML algorithms and models.
- Provides support for recurrent neural networks.
The machine learning library also offers command-line programs and C++ classes, which can be integrated into large-scale machine learning solutions. Bindings are available for mlpack for a range of programming languages, including C++, Go, Julia, and Python.
Developer – François Chollet and others
Written using – Python
Since – 2015
Purpose – Deep learning
Keras is listed among the most-used Python machine learning libraries. Used specifically for ANNs, Keras has the ability to run well on both CPU and GPU.
- Has the ability to run on top of R, Theano, and more.
- Facilitates fast deep neural networks experimentation.
- Eases the development of DL models with a high-level set of abstractions.
Besides the standard neural networks, the popular machine learning library also comes with support for convolutional and recurrent neural nets. It has a galore of features for working with images.
Developer – Google Brain Team
Written using – C++, CUDA, and Python
Since – 2015
Purpose – Deep Learning
TensorFlow is an enterprise-ready machine learning library from Google. To facilitate ML model deployment, the popular Python machine learning library features TensorFlow Lite and TensorFlow Serving frameworks.
- Leverages Tensorboard – a web-based visualization tool – that lets you visualize model gradients, parameters, and performance.
- Can run on a vast array of CPUs, GPUs, and TPUs with its flexible architecture.
- Highly stable APIs for C++ and Python and less stable APIs for various other programming languages.
The deep learning library from Google comes with extensive documentation that makes it easier for the devs to get acquainted with its features. TensorFlow is more than a regular library. It is a robust computational framework.
Machine learning is a rapidly evolving field. As such, new ML libraries are entering the market every now and then. Nonetheless, only the worthy ones will stay relevant while the others will come and go. What is/are your favorite machine learning library(ies) from the list? Let us know in the comments.
Hi! I am Pankaj, a full-time content specialist and a part-time programmer and marketer. I love to explore new places and also new ideas. I am an inquisitive person with a passion to learn new skills through every possible opportunity that comes in the way.