Machine learning libraries provide developers and data scientists with resources to build, deploy, and train models that incorporate datasets to generate predictions and take specific actions. The models employ deep learning algorithms for image recognition, language processing, computer vision, and data analysis. These capabilities are the foundation of transformative technologies, from smart robots to AI.
Most programmers make use of libraries to develop applications for various industries such as manufacturing, cybersecurity, transportation, finance, healthcare, etc. In this article, we explore the evolution of ML and some of the most useful open-source software (OSS) machine learning libraries available to developers.
Growth of ML libraries
The use of algorithms and model training in machine learning was introduced in the 1950s. At the time, its applications were minor. However, the fundamental concepts that establish the logic behind ML were proposed by many pioneering mathematicians and scientists, including Alan Turing, Allen Newell, Herbert Simon, and Frank Rosenblatt. Then, as improvements in network and computing performance enabled new innovations such as natural language processing (NLP) and computer vision, ML gradually gained momentum over the decades.
By the 1990s, developers using languages such as Pascal, Fortran, and Lisp had access to a rapidly growing ML library of model preprocessing, training, and monitoring tools.
What is the purpose of the ML library?
Libraries helped eliminate complexity by providing pre-written code to perform multiple ML tasks along with automation. Today's libraries provide a variety of tools (code, algorithms, sequences, frameworks, etc.) for building and deploying ML. Machines rely on effective models to learn gradually and mature autonomously without active intervention by programmers. To that end, hundreds of different ML libraries exist, each with their own strengths and capabilities to simplify the implementation of complex algorithms and test advanced models.
Making the most of ML libraries
Generally, most ML libraries are written in C++, but developers should be familiar with multiple languages. This versatility allows them to choose the best language for different tasks, resulting in better results. For example, Python gives programmers the advantage of developing AI applications, working with GPUs, and reducing overall development time. On the other hand, coding in C++ can be more effective for certain projects, such as building small neural networks and improving model performance.
Additionally, the business and computing problems a developer is trying to solve can determine the most effective language to use. Other effective languages for ML programming include JavaScript, R, Julia, Go, Java, and the perennial favorites C and C++.
Top OSS Libraries
These 10 popular ML libraries provide essential resources for designing, building, and deploying effective models.
1. TensorFlow
TensorFlow open source library developed by Google focuses on deep learning, which is required for building neural networks, developing image recognition, and creating NLP systems. Programmers can use the library's automatic differentiation to optimize model performance. In addition, TensorFlow can be used to improve feature gradient calculations, which determine how fast a model learns.
2. Pie Torch
Developed by Meta, PyTorch streamlines model prototyping by using tensors (multidimensional arrays) that process data more efficiently. Tensors support automatic differentiation, which is an essential prerequisite for training deep learning models for robotics, computer vision, NLP, and many other applications. Additionally, PyTorch's dynamic computational graph allows programmers to debug and modify models in real time. Finally, the PyTorch forum community is very supportive of beginners.
3. Psychiatryarn
Scikit-learn is great for understanding the fundamentals of ML. Developers can learn the difference between supervised and unsupervised learning, understand linear and nonlinear model choices, and perform validation techniques. The intuitive GUI helps users understand the relationship between inputs and outputs, which defines how data is represented, processed, and transformed into predictions for consumer behavior, market trends, and IT management.
4. Keras
Keras employs seamless deep learning integration with TensorFlow to simplify model building and training. For developers working in Python, the library provides an effective interface that abstracts away the complexities of creating neural networks and a simplified API, making ML development accessible to beginners.
5. Apache MXNet
MXNet supports major ML languages (Python, R, Scala, Julia, Matlab, JavaScript) and compiles to C++ for fast execution and overall reliability. Programmers can build Convolutional Neural Networks (CNNs) that classify images into various predefined categories, with the added benefit of being able to deploy lightweight neural network models on low-power devices, from desktop PCs to cloud servers.
6. Jacks
Developed by Google and known for its speed, Jax provides high performance with just-in-time compilation, efficient model training with gradient descent computation, and dynamic scalability, making it suitable for large-scale ML operations. Additionally, it allows developers and data scientists to harness the power of hardware accelerators (GPUs and Tensor Processing Units) to speed up computation of deep learning models.
7. Hug Face Transformer Library
Hugging Face is known as the GitHub of ML, where developers and data scientists can build, train, and deploy ML models. Using the huggingface_hub Python library client, a subset of the Hugging Face Transformers library, programmers can access development support, numerous models, data sets, demos, and support for dozens of additional libraries. As an open-source public repository, it is continually growing as thousands of developers iterate and improve the code. In addition to language models, Hugging Face also provides computer vision, audio, and image models.
8. ML.NET
Developed by Microsoft, the ML.NET open source framework is fully integrated with the .NET ecosystem, providing native tools and APIs for building and deploying ML models. It offers diverse functions for classification, regression, clustering, and anomaly detection. ML.NET adopts the Open Neural Network Exchange as a common format for transferring models between different ML formats. Deployed across industries such as healthcare, finance, and e-commerce, ML.NET can be challenging for new developers due to limited, though growing, community support.
9. Shogun
Shogun is an accessible and easy-to-use tool for data scientists, programmers, and students. Specifically designed to handle large data sets, this open source ML library provides a combination of ML algorithms, data structures, and versatile tools for prototyping data pipelines and building models. It automatically generates build interfaces depending on your development language of choice, including Python, Java, Ruby, C#, R, and Lua. First introduced in 1999, Shogun features an active and supportive community.
10. Panda
Pandas is ideal for creating DataFrames, a spreadsheet-like data structure that offers flexibility in storing and manipulating data. This popular library is also useful for exploratory data analysis, a key step in ensuring a reliable ML implementation that can deliver the insights you need. Because it is built on Python, knowledgeable developers have easy access to resources for grouping, combining, and filtering a variety of data.
Kerry Doyle writes about technology for a variety of publications and platforms, currently focusing on a range of issues relevant to IT and enterprise leaders, from nanotech and cloud to distributed services and AI.
