The field of AI development offers a range of tools and frameworks that can simplify and enhance the process of building intelligent applications. Here are 22 popular AI development tools:

1. TensorFlow: An open-source machine learning framework by Google that supports a wide range of tasks, including neural networks and deep learning.
2. PyTorch: An open-source machine learning library that provides dynamic computational graphs and is widely used for natural language processing and computer vision.
3. Keras: A high-level neural networks API written in Python that runs on top of TensorFlow, Theano, or Microsoft Cognitive Toolkit.
4. scikit-learn: A powerful machine learning library for Python that provides a wide array of algorithms and tools for data mining and analysis.
5. Caffe: A deep learning framework that focuses on speed and efficiency, commonly used for image classification and other vision tasks.
6. Theano: A Python library that allows for efficient mathematical operations and is widely used for deep learning research.
7. MXNet: A flexible and efficient deep learning framework that supports both imperative and symbolic programming models.
8. Microsoft Cognitive Toolkit (CNTK): A deep learning framework developed by Microsoft that offers high performance and scalability.
9. IBM Watson: A suite of AI-powered services and APIs offered by IBM, including language understanding, speech recognition, and computer vision.
10. H2O.ai: An open-source machine learning platform that provides automated machine learning, model interpretability, and scalable deployment capabilities.
11. Apache Mahout: A scalable machine learning and data mining library that provides various algorithms for clustering, classification, and recommendation.
12. Apache Spark MLlib: A scalable machine learning library built on Apache Spark that supports distributed computing and provides algorithms for classification, regression, and clustering.
13. RapidMiner: A data science platform that offers a wide range of tools for data preparation, machine learning, and predictive analytics.
14. BigML: A cloud-based machine learning platform that simplifies the process of building and deploying machine learning models.
15. Amazon SageMaker: A fully managed machine learning service provided by Amazon Web Services (AWS) that enables the development, training, and deployment of ML models at scale.
16. DataRobot: An automated machine learning platform that streamlines the process of building and deploying machine learning models.
17. KNIME: An open-source data analytics platform that allows users to visually design and execute data workflows, including machine learning and AI tasks.
18. Google Cloud AI Platform: A cloud-based platform by Google that provides various AI services, including training and deploying machine learning models.
19. IBM Watson Studio: A collaborative environment for data scientists that enables data exploration, model building, and deployment.
20. Nvidia Deep Learning SDK: A software development kit that provides GPU-accelerated libraries and tools for deep learning applications.
21. Microsoft Azure Machine Learning: A cloud-based platform by Microsoft that offers a comprehensive set of tools and services for building and deploying ML models.
22. Ludwig: An open-source deep learning toolbox that allows users to train and test models without writing code, developed by Uber AI Labs.

These are just a few examples of the wide range of AI development tools available. The choice of tool depends on your specific needs, skillset, and project requirements.

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