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Google's TensorFlow offers flexible tools for large-scale AI applications and supports both deep and traditional machine learning.
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PyTorch’s dynamic computation graphs make it ideal for experimentation, widely used in research for deep learning tasks.
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Perfect for beginners, Scikit-learn simplifies traditional ML algorithms and integrates seamlessly with Python libraries.
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Keras is a user-friendly deep learning API, ideal for fast prototyping with seamless TensorFlow integration.
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H2O.ai’s AutoML feature enables quick, high-quality models and integrates well with big data tools like Spark.
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Known for speed and accuracy, XGBoost is a go-to for powerful predictive tasks in large datasets.
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LightGBM is fast, efficient, and perfect for high-dimensional data tasks like fraud detection and ranking systems.
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MLlib offers scalable machine learning solutions, perfect for large datasets and enterprise-level applications.
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Caffe excels in image processing, offering speed and modularity for classification and recognition tasks.
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ONNX enables model portability between frameworks, ensuring flexibility in AI training and deployment.