Top 5 Machine Learning Libraries

Top 5 Machine Learning Libraries

In this post, we will discuss the most popular and important ML/DL libraries in the world right now.

Originally published by Chidume Nnamdi at https://blog.bitsrc.io

Machine Learning has grown in the past few years at a very rapid rate. This is due to the release of Machine Learning (ML)/Deep Learning (DL) libraries that abstracts away the huge complexity of scaffolding or implementing an ML/DL model.

ML/DL involves a lot of mathematical calculations and operationsת especially Matrix. These ML/DL makes it very easy for a complete noob in ML to start it up like a pro. The very first day I used Tensorflow I was awed by the amount of Matrix operations and mathematics I could skip-over, which was done for me by the library, I was able to build and train a XOR model at the very first attempt.

Machine learning, sometimes abbreviated to “ML,” uses general-purpose mathematical models to answer specific questions using data. Machine learning has been used to detect spam email, build highly-smart missiles, build intelligent robots, detect objects through computer vision, build intelligent homes, recognize speech, build a system that can write (novels, poetry, etc), recommend products to customers, and predict the value of commodities for many years.

In this post, we will discuss the most popular and important ML/DL libraries in the world right now.

TensorFlow

This is the most popular ML/DL in the world today, it wasn’t the first, but when it came with its simplicity. It grew rapidly to surpass the already existing libraries. It was because of its easy-to-use APIs. You could guess, it was released by Google in November 2015.

It is written in Python, but now we have a JavaScript port of it tensorflow.js.

This was due to the upsurge of JavaScript following the advent of Node.js.

According to Wikipedia:
TensorFlow is a free and open-source software library for dataflow and differentiable programming across a range of tasks. It is a symbolic math library, and is also used for machine learning applications such as neural networks.

Theano

Theano is a Python library for fast numerical computation that can be run on the CPU or GPU. It was developed by the LISA (now MILA) group at the University of Montreal, Quebec, Canada. It is named after a Greek mathematician, Theano.

According to Wikipedia:
Theano is a Python library and optimizing compiler for manipulating and evaluating mathematical expressions, especially matrix-valued ones.

PyTorch

This is a Facebook deep-learning library. PyTorch was built by Facebook, as it name implies it was written in Python.

Compared to Tensorflow, it is easier to learn and use but as you can guess it was beaten by Tensorflow. This is because Tensorflow encompasses a wide-range of stuff in ML/DL while PyTorch has a few. Nonetheless, PyTorch provide a simpler API for working with Neural Networks.

According to Wikipedia:
PyTorch is a deep learning library based on the Torch library, used for applications such as computer vision and natural language processing. It is primarily developed by Facebook’s artificial intelligence research group. It is free and open-source software released under the Modified BSD license.

Scikit-learn

This is a popular ML library, built on NumPy, SciPy and matplotlib. It focuses mostly on ML algorithms:

  • Supervised learning
  • Unsupervised learning
  • Linear regression
  • Logistic regression
  • SVM
  • Naive Bayes
  • Gradient boosting
  • Clustering
  • K-Means

Like PyTorch it is less mature to Tensorflow, but it provides simple and efficient tools for data mining and data analysis.

According to Wikipedia:
Scikit-learn (formerly scikits.learn) is a free software machine learning library for the Python programming language.[3] It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.

Keras

Keras is a DL library that wraps around the functionalities of othe 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.

According to Wikipedia:

Keras is an open-source neural-network library written in Python. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, Theano, or PlaidML. Designed to enable fast experimentation with deep neural networks, it focuses on being user-friendly, modular, and extensible.

Conclusion

There are many more tools in ML/DL world, but these are the most popular and widely used. ML/DL is a huge world and the most promising tech right now .

If you have any question regarding this or anything I should add, correct or remove, feel free to comment, email or DM me.

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Further reading

Machine Learning A-Z™: Hands-On Python & R In Data Science

Python for Data Science and Machine Learning Bootcamp

Machine Learning, Data Science and Deep Learning with Python

Deep Learning A-Z™: Hands-On Artificial Neural Networks

Artificial Intelligence A-Z™: Learn How To Build An AI

A Complete Machine Learning Project Walk-Through in Python

Machine Learning: how to go from Zero to Hero

Top 18 Machine Learning Platforms For Developers

10 Amazing Articles On Python Programming And Machine Learning

100+ Basic Machine Learning Interview Questions and Answers



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