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Top 10 toolkits/libraries for AI/ML in 2023

Somya Kabra •
#AI#programming#ML#Python

Tools

Having experimented with numerous software applications, tools, and Python libraries, I have identified a selection that I believe are highly beneficial for individuals who are new to the field.

TensorFlow, developed by Google, is a popular open-source library for machine learning and deep learning. It provides a comprehensive ecosystem of tools, libraries, and community resources that enable developers to build and deploy AI models with ease. With TensorFlow, you can create neural networks, handle data preprocessing, and perform complex computations efficiently.  

PyTorch is another powerful deep learning framework widely used by researchers and developers. Developed by Facebook’s AI research lab, PyTorch offers a dynamic computational graph and extensive support for neural networks. Its intuitive interface makes it easier for developers to experiment with different architectures and optimise models.  

Scikit-learn is a versatile Python library that provides efficient tools for data mining, data analysis, and machine learning. It offers a wide range of algorithms, including classification, regression, clustering, and dimensionality reduction. Scikit-learn’s simplicity and consistency make it a favourite choice for developers, particularly those new to AI.  

Keras is a user-friendly deep learning library that sits on top of TensorFlow. It simplifies the process of building neural networks by providing high-level abstractions and a modular architecture. Keras supports both convolutional and recurrent networks and offers an intuitive API for defining models, compiling them, and training them efficiently.  

Apache Mahout is a distributed linear algebra framework built on top of Apache Hadoop. It provides a scalable and efficient environment for implementing machine learning algorithms, particularly for large-scale datasets. Mahout offers a rich set of algorithms, including clustering, classification, and collaborative filtering, enabling developers to leverage big data capabilities for AI tasks.  

The Microsoft Cognitive Toolkit, also known as CNTK, is a deep learning library developed by Microsoft Research. CNTK offers excellent performance and scalability, making it suitable for both research and production environments. It supports various neural network types and provides powerful tools for training models on CPUs, GPUs, and distributed systems.  

H2O.ai is an open-source platform that simplifies the implementation of AI and machine learning models. It supports popular programming languages like Python and R, offering a wide range of algorithms and automated machine learning capabilities. H2O.ai’s user-friendly interface and extensive documentation make it accessible to developers of all skill levels.  

OpenCV (Open Source Computer Vision Library) is a widely used computer vision and image processing library. It provides a comprehensive collection of algorithms and functions for tasks such as object detection, image recognition, and video analysis. OpenCV’s cross-platform support and extensive documentation make it an essential tool for AI developers working with visual data.  

RapidMiner is a powerful data science platform that facilitates end-to-end AI workflows. It offers an intuitive visual interface for building predictive models, performing data preprocessing, and deploying AI solutions. RapidMiner supports a wide range of algorithms and provides advanced features like automatic model optimisation and ensemble learning.  

Jupyter Notebook is an interactive development environment that allows developers to create and share documents containing live code, equations, visualisations, and narrative text. It supports various programming languages, including Python, R, and Julia, making it an excellent tool for prototyping and experimenting.  

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