Learn about some of the best Python libraries for artificial intelligence (AI), machine learning (ML), and deep learning (DL) programming.
Many software developers are attracted to Python because it has a huge collection of open source libraries. Recently, many libraries have emerged in the areas of machine learning (ML) and artificial intelligence (AI). These libraries can be easily used by programmers of all levels for tasks such as data science, image and data manipulation. This programming tutorial reveals why Python is the preferred language for machine learning and AI, and lists some of the best ML and AI libraries to choose from.
Why choose Python for AI development
Paul Dubois, lead developer of Numerical Python and Pyfort, once said, “Python is still the most powerful language you can read.” Another feature that has propelled Python to where it is today is its versatility and flexibility, allowing Python to be used with other programming languages, including powerful languages such as Java and C#, if desired. increase. Moreover, Python can run on almost any OS and platform on the market.
This may explain Python’s enduring popularity among developers, but why do so many developers choose Python to work with ML and AI libraries? , with the sheer number of ML and AI libraries available, developers are sure to find whatever problem they need to solve. Additionally, Python is an object-oriented programming (OOP) language, making it particularly well-suited for efficient use and manipulation of data.
Here are some more reasons why Python is one of the top programming languages for machine learning, deep learning, and artificial intelligence.
- Being free and open source makes Python friendly to the community and guarantees improvements in the long run
- Extensive library ensures you find a solution to any problem
- Smooth implementation and integration make it accessible to people of different skill levels
- Increase productivity by reducing coding and debugging time.
- Can also be used for soft computing and natural language processing
- Works seamlessly with C and C++ code modules
We’ve explained why Python is one of the top programming languages. The rest of this article introduces some of the best Python libraries for machine learning and AI.
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Numpy
Formerly known as “Numeric”, NumPy was the brainchild of Jim Hugunin, with contributions from several other developers. NumPy was officially born in 2005 when Travis Oliphant incorporated the competing functionality of his Numarray into his Numeric with significant changes. NumPy is now fully open source and has many contributors. It is also widely recognized as the best Python library for machine learning and AI.
NumPy is primarily used by data scientists to perform various mathematical operations on large multidimensional arrays and matrices. NumPy arrays require much less storage space than other Python lists, are fast and easy to use, making them a great option for improving the performance of machine learning models without too much effort. Another attractive feature is that NumPy has tools for integrating C, C++, and Fortran code.
NumPy has several other features that are popular among the scientific community.
- Support for mathematical and logical operations
- shape manipulation
- Sorting and selection features
- Discrete Fourier Transform
- Basic linear algebra and statistical operations
- random simulation
- n-dimensional array support
Cypee
NumPy (see above) is so popular that several libraries are based on it, including SciPy. Like its inspiration, SciPy is also a free and open source library. SciPy is intended for large data sets and performing scientific computing on those data sets. SciPy also comes with built-in modules for array optimization and linear algebra, similar to NumPy. SciPy has played an important role in scientific analysis and engineering and has grown to be one of the fundamental Python libraries.
The beauty of SciPy is that it takes all the functionality of NumPy and turns it into an easy-to-use scientific tool. As such, it is often used for image manipulation and provides basic processing capabilities for high-level, non-scientific mathematical functions.
The main features of SciPy are:
- user friendly
- Data visualization and manipulation
- Scientific and technical analysis
- computes large datasets
TensorFlow
TensorFlow is a free, open source library available in Python, JavaScript, C++, and Java. This flexibility lends itself to a wide range of applications in various fields. Developed by the Google Brain team for his internal Google use in research and production, the first version was released in 2015 under the Apache License 2.0. Google released an updated version of his TensorFlow named TensorFlow 2.0 in September 2019.
TensorFlow can be used for many different tasks, but it’s especially good at training and inferring deep neural networks. TensorFlow enables developers to create and train ML models not only on computers, but also on mobile devices and servers using TensorFlow Lite and TensorFlow Serving. These alternatives offer the same benefits except they target mobile platforms and high-performance servers.
ML and DL areas where TensorFlow excels include:
- Processing deep neural networks
- natural language processing
- partial differential equation
- Abstraction function
- Image, Text, Speech Recognition
- Easily collaborate on ideas and code
Final Thoughts on Python AI and Machine Learning Libraries
In this tutorial, we clarified why Python is the preferred language for machine learning and AI, and listed some of the best ML and AI libraries to choose from, including TensorFlow, SciPy, and NumPy. We’ll be adding to this list in the coming weeks, so check back often.
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