Here are essential tools and platforms for AI/ML development:

🧠 Core Frameworks & Libraries

  • TensorFlow

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    Open-source framework by Google for machine learning and deep learning. [Explore TensorFlow tutorials](/en/tutorials/tensorflow)

  • PyTorch

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    Popular library for dynamic neural networks (used by Facebook AI Research). [Check PyTorch documentation](/en/docs/pytorch)

  • scikit-learn

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    Python library for classical machine learning algorithms. [See scikit-learn examples](/en/examples/scikit-learn)

🛠️ Development Tools

  • Jupyter Notebook

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    Interactive coding environment for data science workflows. [Try Jupyter online](/en/jupyter)

  • Google Colab

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    Free cloud-based notebook with GPU/TPU support for ML experiments. [Launch Colab demo](/en/colab)

🏗️ Cloud Platforms

  • AWS SageMaker

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    Fully managed service for building and deploying ML models. [AWS ML services overview](/en/aws-ml)

  • Azure Machine Learning

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    Cloud platform for end-to-end ML development (Microsoft's offering). [Azure ML documentation](/en/azure-ml)

For deeper exploration, browse AI/ML software categories to discover more resources. 🌐