- TensorFlow:
TensorFlow is an open-source machine learning framework developed by Google. It is widely used for various AI applications, including deep learning and neural networks.
2. PyTorch:
PyTorch is another popular open-source deep learning framework. It is known for its dynamic computation graph, making it more intuitive for researchers and developers.
3. Scikit-learn:
Scikit-learn is a machine learning library for Python that provides simple and efficient tools for data analysis and modeling. It includes various algorithms for classification, regression, clustering, and more.
4. Jupyter Notebooks:
Jupyter Notebooks are an open-source web application that allows you to create and share documents that contain live code, equations, visualizations, and narrative text. They are widely used in AI development for interactive and collaborative work.
5. Keras:
Keras is a high-level neural networks API written in Python. It can run on top of TensorFlow, Theano, or Microsoft Cognitive Toolkit. Keras simplifies the process of building and experimenting with deep learning models.
6. IBM Watson Studio:
IBM Watson Studio is a comprehensive platform for AI and data science. It offers tools for data preparation, model development, and deployment. It supports various languages and frameworks.
7. Microsoft Azure Machine Learning:
Azure Machine Learning is a cloud-based service by Microsoft that provides tools for building, training, and deploying machine learning models. It supports popular frameworks like TensorFlow and PyTorch.
8. GitLab Auto DevOps:
GitLab Auto DevOps incorporates CI/CD (Continuous Integration/Continuous Deployment) practices with built-in automation. While not solely AI-based, it leverages automation to streamline the software development lifecycle.
9. Amazon SageMaker:
SageMaker is a fully managed service by Amazon Web Services (AWS) that enables developers to build, train, and deploy machine learning models at scale. It simplifies the machine learning workflow.
10. DeepCode:
DeepCode uses machine learning to analyze code and provide suggestions for improvements, bug fixes, and security vulnerabilities. It assists developers in writing cleaner and more efficient code.
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