{"id":1345,"date":"2021-07-20T15:29:48","date_gmt":"2021-07-20T15:29:48","guid":{"rendered":"https:\/\/jenniferkwentoh.com\/?p=1345"},"modified":"2022-02-07T15:24:19","modified_gmt":"2022-02-07T15:24:19","slug":"best-machine-learning-libraries","status":"publish","type":"post","link":"https:\/\/jenniferkwentoh.com\/best-machine-learning-libraries\/","title":{"rendered":"10 Best Open-source Machine Learning Libraries [2022]"},"content":{"rendered":"\n

Machine learning<\/a> libraries and frameworks make it easier to write code for machine learning<\/a> without knowing the underlying mathematics behind the algorithms or building from scratch. With libraries, we can write code faster to train models.<\/p>\n\n\n\n

In picking a machine learning<\/a> framework to use, carefully consider these:<\/p>\n\n\n\n

1. Learning curve of a machine learning library<\/strong><\/p>\n\n\n\n

Some machine learning libraries are easy to learn and implement, while others require more technical expertise.<\/p>\n\n\n\n

2. User and organization’s adoption<\/strong><\/p>\n\n\n\n

It is essential to know what tool organizations use in production if you intend to apply for machine learning jobs or build your product. <\/p>\n\n\n\n

3. Project scope<\/strong><\/p>\n\n\n\n

Machine learning libraries focus on different goals. Find a library that is useful to your project scope. <\/p>\n\n\n\n

If your project scope focuses on image data, you should choose better-optimized frameworks for images.<\/p>\n\n\n\n

10 best machine learning libraries and frameworks.<\/h2>\n\n\n\n

1. PyTorch<\/h2>\n\n\n\n
\"machine_learning_libraries_pytorch-logo\"<\/figure><\/div>\n\n\n\n

PyTorch<\/a> is an open-source machine learning framework developed by Facebook’s AI Research lab (FAIR)<\/p>\n\n\n\n

Written in: Python, CUDA, C++.<\/p>\n\n\n\n

PyTorch is used both for research and production in building state-of-the-art products. It broadly supports the development of projects in computer vision, natural language processing, reinforcement<\/p>\n\n\n\n

learning and more.<\/p>\n\n\n\n

It has a robust ecosystem and is supported on major cloud platforms.<\/p>\n\n\n\n

Learning curve: Medium<\/strong><\/h3>\n\n\n\n

PyTorch is easier to learn than other deep learning frameworks.<\/p>\n\n\n\n

Adoption level: High<\/strong><\/h3>\n\n\n\n

1900+ contributors on Github. Used by over 83,000 repositories on Github.<\/p>\n\n\n\n

Who is using PyTorch?<\/h3>\n\n\n\n

salesforce, Stanford university, Udacity<\/p>\n\n\n\n

Where to learn PyTorch<\/h3>\n\n\n\n

Learn the basics of PyTorch following these tutorials.<\/a><\/p>\n\n\n\n

2. TensorFlow (TF)<\/h2>\n\n\n\n
\"machine_learning_libraries_tensorflow\"<\/figure><\/div>\n\n\n\n

TensorFlow<\/a> is an open-source platform for machine learning developed by Google. TensorFlow was released to the public in November 2015. The core of TensorFlow is written in Python, C++, and CUDA.<\/p>\n\n\n\n

TF is used both in research and production environment.<\/p>\n\n\n\n

Although Python is widely used for TensorFlow, TensorFlow is available in R, JavaScript.<\/p>\n\n\n\n

TF is popularly used for numerical computations. It has inbuilt machine learning and statistical tools. It is used to build projects on regression, classification, neural networks, and more.<\/p>\n\n\n\n

In production, TensorFlow Extended (TFX) is used to build a production pipeline<\/a>. It is optimized for large scaling and other deployment features.<\/p>\n\n\n\n

Read more on MLOps.<\/a><\/p>\n\n\n\n

TensorFlow has a visualization toolkit called TensorBoard<\/strong>. TensorBoard provides an interactive web-based dashboard for visualization.<\/p>\n\n\n\n

TensorFlow can run computations on CPU, GPU, and TPU.<\/p>\n\n\n\n

Learning curve: Medium<\/h3>\n\n\n\n

Adoption level: High<\/strong><\/h3>\n\n\n\n

TensorFlow is widely used in production.<\/p>\n\n\n\n

3,036+ Contributors; used by over 146,000 repositories on Github.<\/p>\n\n\n\n

Examples<\/h3>\n\n\n\n

TensorFlow has a step-by-step example on its website.<\/p>\n\n\n\n