What is a machine learning library?
A machine learning 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.
Which libraries are used for machine learning?
Numpy: Machine Learning Libraries For Scientific Computation
Numpy or numerical Python is arguably one of the most important Python packages for Machine Learning. Scientific computations use a ton of matrix operations. And these operations can be pretty computationally heavy. Implementing them naively can easily lead to inefficient memory usage.
Numpy arrays are a special class of arrays that do these operations within milliseconds. These arrays are implemented in C programming language. In tasks like Natural Language Processing where you have a large set of vocabulary and hundreds of thousands of sentences, a single matrix can have millions of numbers. As a beginner, you have to master using this library.
Pandas: Machine Learning Libraries For Tabular Data
In simple terms, Pandas is the Python equivalent of Microsoft Excel. Whenever you have tabular data, you should consider using Pandas to handle it. The good thing about Pandas is that doing operations is just a matter of a couple of lines of code. If you want to do something complex, and you find yourself thinking about a lot of code, there is a high probability that there exists a Pandas command to fulfill your wish in a line or two.
Scikit Learn: Machine Learning Libraries For Data Preprocessing & Modelling
Scikit Learn is perhaps the most popular library for Machine Learning. It provides almost every popular model – Linear Regression, Lasso-Ridge, Logistics Regression, Decision Trees, SVMs and a lot more. Not only that, but it also provides an extensive suite of tools to pre-process data, vectorizing text using BOW, TF-IDF or hashing vectorization and many more.
It has huge support from the community. The only drawback is that it does not support distributed computing for large scale production environment applications well. If you wish to build your career as a Data Scientist or Machine Learning Engineer, this library is a must!
Statsmodels: Machine Learning Libraries For Time Series Modeling
Statsmodels is another library to implement statistical learning algorithms. However, it is more popular for its module that helps implement time series models. You can easily decompose a time-series into its trend component, seasonal component, and a residual component.
You can also implement popular ETS methods like exponential smoothing, Holt-Winters method and models like ARIMA and Seasonal ARIMA or SARIMA. The only drawback is that this library does not have a lot of popularity and thorough documentation as Scikit.
Regex: Machine Learning Libraries For Text Processing
Regular expressions or regex is perhaps the simplest yet the most useful library for text processing. It helps find text according to defined string patterns in a text. For example, if you wish to replace all the ‘can’t’s and ‘don’t’s in your text with cannot or do not, regex can do it in a jiffy.
If you wish to find phone numbers in your text, you just have to define a pattern and regular expressions with return all the phone numbers in your text. It not only can find patterns but can also replace it with a string of your choice. Making correct matching patterns can be a little confusing in the beginning, but once you get a hang of it, its fun!
NLTK: Machine Learning Libraries for Natural Language Tasks
NLTK or Natural Language Toolkit is an extensive library for Natural Language tasks. It is a go-to package for all your text processing needs – from word tokenization to lemmatization, stemming, dependency parsing, chunking, stopwords removal and many more.
Tensorflow: Machine Learning Libraries For Deep Learning
Tensorflow is by far currently the most popular library with extensive documentation and developer community support. It was created by Google. For product-based companies, Tensorflow is a no brainer because of the ecosystem it provides for model prototyping to production. Tensorboard, a web-based visualization tool helps developers to visualize model performance, model parameters and gradients.
A major criticism about Tensorflow in the community is its implementation of graphs. A graph is a set of operations you define. For example, c = a+b, d = c*c is a graph the does two operations on 4 variables. In python, you can perform the first step, get the value of c and then use it to calculate d. In Tensorflow, you have to compile the graph first. This means Tensorflow will first arrange all the operations and then execute them all at once.
Unlike Python which is define by run, Tensorflow is define and run. This makes debugging cumbersome. In the recent Tensorflow summit, they have made changes to enable the define by run mode using eager execution. However, when it comes to the production environment, Tensorflow provides frameworks like Tensorflow Lite (for mobile devices) and TensorFlow Serving for deploying models.
Pytorch: Machine Learning Library For Deep Learning
In a single line, Pytorch is everything Tensorflow is not. It was developed by Facebook as a Pythonic version of the original library Torch, which is a deep learning framework written for Lua programming language.
Unlike Tensorflow, it was designed to be as Pythonic as possible. One major way in which it blows Tensorflow out of water is its execution of Dynamic Graphs. You can define your model components on the go. This is a blessing if you want to do research where you need this kind of flexibility with low-level APIs.
Armadillo: Machine Learning Library 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. 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.
FANN: Machine Learning Library 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,
- Environmental sciences,
- 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.
Keras: Machine Learning Library 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,
- Objectives, and
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.
Keras offers a high-level, intuitive set of abstractions for easing the development of deep learning models. There is also superb community support and extensive support available in TensorFlow’s core library.
OpenNN: Machine Learning Library 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.
OpenNN is capable of implementing any number of layers of non-linear processing units for supervised learning. It enables multiprocessing programming using OpenMP and features data mining algorithms as a bundle of functions integrated into other software tools through an API.
More than just a library, OpenNN can be used as a general-purpose AI software package.
Shogun: Machine Learning Library For Software Libraries
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.
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.
Theano: Machine Learning Library 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,
- 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.
There are many highlights about Theano. Firstly, it’s excellent at avoiding bugs and errors automatically while working with exponential and logarithmic functions. It’s also efficient at symbolic differentiation and at evaluating expressions faster with dynamic C code generation.
What is the best library for machine learning?
It entirely depends on what tasks you plan to accomplish with the library. There is no absolute best machine learning library.
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.