With the use of Machine Learning (ML) on the rise, it is more important than ever to take a look at the leading five ML libraries being used today. But before we get into that, let’s look at what is an ML library? A Machine Learning library, or a Machine Learning framework, is a set of routines and functions that are written in a given programming language. Essentially, they are interfaces, libraries or tools helping developers to easily and quickly build machine learning models, going past the specific basic details of the underlying algorithms. So they basically help developers carry out complex tasks without having to rewrite many lines of code. Now let us look at the five best ML libraries out there for developers today: 1. TensorFlow Created by the Google Brain team, TensorFlow is a free and open-source software library used for research and production. Allowing easy and effective implementation of machine learning algorithms, it is an efficient math library and is also used for machine learning applications such as neural networks. The emergence of high-level APIs (Application Programming Interfaces) like Keras and Theano has made TensorFlow more effective in improving the capability of computers to predict solutions with a greater degree of accuracy. Bear in mind that TensorFlow offers stable APIs for Python and C. Providing parallel processing, it is easily trainable on CPU as well as GPU (Graphics Processing Unit) for distributed computing and enjoys a large community support with additional advantages including better computational graph visualizations, quick updates, frequent new releases with new features, good debugging methods and scalability. 2. Keras Several back-end neural network computation engines are supported by Keras, an open-source neural network library written in Python. It can run on top of frameworks such as TensorFlow, Microsoft Cognitive Toolkit, Theano. Keras has many impressive features. First is modularity, where a model can be understood as a sequence or a graph alone, next is minimalism so that the library shares just enough to get an outcome and there’s also the element of maximizing readability and extensibility which allows researchers to do more trials. Its advantages include its support for a wide range of production deployment options and integration with back-end engines/frameworks; it also helps that everything in Keras is native Python. Kids and teens interested in learning TensorFlow and Keras can join the YoungWonks afterschool coding program. Here, they will first get to learn the basics of Python and work their way up to learn about the two ML libraries in live online classroom sessions focusing on project-based and self-paced learning. 3. Scikit-learn Scikit-learn is a free machine learning library for Python built on SciPy. An effective tool for data mining and data analysis, it is used today for model selection, clustering, preprocessing, and more. Its popularity can be traced to the fact that it boasts a clean API, is easy to use, fast, comprehensive and enjoys good documentation and the support of an active developer community. It also scores well on the simplicity and accessibility front. 4. Theano Also a Python ML library, Theano is an open-source project developed by Montreal Institute for Learning Algorithms (MILA) at the Université de Montréal. It allows developers to define, optimize and evaluate mathematical expressions that include multi-dimensional arrays. It provides features such as good integration with NumPy, transparent use of a GPU, extensive unit-testing, and self-verification. 5. PyTorch Developed by Facebook’s AI Research lab (FAIR), PyTorch is used for applications like computer vision and natural language processing. Also a free and open-source software, it has a polished Python interface along with a C++ interface. PyTorch offers Tensor computing (like NumPy) with strong acceleration via graphics processing units (GPU) and today many deep learning softwares have been/ are being built on top of PyTorch.
May-18-2021, 08:20:39 GMT