Whenever we talk about the latest technologies that are taking over the world, then Machine Learning and AI (Artificial Intelligence) is the first choice, undoubtedly. With the increasing popularity of automation, ML & AI are occupying the industry like wildfire. Thus, it becomes very essential to be equipped with these technologies in order to excel in the IT industry.
And when we talk about Machine Learning, then languages like Python offer a multitude of Machine Learning Libraries that laid the foundation for the development of AI-based software solutions.
Photo by Franck V. / Unsplash - machine learning robot
And when we talk about the technological advancements, then Machine Learning comes out as the best option to upskill yourself. Thus, we have curated a list of Top 5 Machine Learning Libraries in 2020.
According to Wikipedia, TensorFlow is a free and open-source programming construct, often referred to as a library for data-flow and differentiable programming which is employed across a wide array of tasks. It is a library which is used for machine learning applications such as neural networks, fuzzy logic, and genetic algorithms.
Tensorflow is, by far, one of the most popular Machine Learning libraries in the world today, it wasn’t the first one to be used, but when it was launched in the market, due to the ease of usage and simple syntax, it witnessed a great upsurge and rapidly to surpassed all the libraries that existed in the market.
Scikit-Learn is one of the most dynamic and widespread machine learning libraries for classical ML algorithms. It is built on top of two basic Python libraries, which are, NumPy and SciPy. Scikit-Learn provides sustenance to most of the supervised and unsupervised learning algorithms. This library can also be used for data-mining, data gathering, and data analysis, which makes it a great tool who is starting with ML.
Scikit-learn is a free-of-cost machine learning library attributed to Python. It features various algorithms including classification, regression and clustering algorithms alobg with support vector machines, gradient boosting, random forests, k-means, etc.
Needless to say, Machine Learning is primarily composed of mathematics and statistical computations. Theano is a popular ML library of the Python programming language that is used to define, evaluate, and optimize arithmetic equations, mathematical expressions, and statistical computations adequately involving multi-dimensional arrays.
It is achieved by optimizing the CPU utilization. Unit-testing, self-verification to detect and diagnose a multitude of errors, are some of the areas in which Theano is used extensively. Theano is a robust library that has widespread usage in the large-scale mathematical and computation-intensive scientific projects. Theano is complex yet simple and adaptable enough to be used by beginners and individual developers for their projects.
Keras is a substantial Machine Learning library for Python. It is a high-level neural networks API which has the potential of running on top of TensorFlow, CNTK, or Theano. It can run smoothly on CPU and GPU indifferently. Keras makes it effortless for ML beginners to build, design, and construct a Neural Network. Easy and quick prototyping is a strong characteristic of Keras.
Keras is a deep learning library that wraps around the functionalities of other libraries like Tensorflow, Theano, or CNTK. Written in Python. Keras has upper-hand on its competitors like Scikit-learn and PyTorch because it runs on top of Tensorflow.
PyTorch is yet another open-source Machine Learning library built in Python, which is based on Torch(which is an open-source Machine Learning library which is implemented in C with a wrapper in Lua).
It has an extensive option of tools to choose from and libraries that provides assistance on Computer Vision, Natural Language Processing(NLP), and many more ML programs. It allows developers to perform computations on Tensors with GPU acceleration and also helps in creating computational graphs. It was primarily developed by Facebook’s artificial intelligence research group.
Thus, in a nutshell, we can say that the above listed Machine Learning libraries are essential if you want to get a good hands-on experience in machine learning, then these are a must for you to get acquainted with. The libraries are inter-related to each other and form the basis for the complex Machine Learning algorithms. Hence, you must certainly learn these libraries if you want to make a career in Machine Learning and excel in the same.