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Machine Learning Libraries
High-Level Neural Networks API
★ 4.7
Deep Learning Framework
★ 4.8
pip install keraspip install torchpip install keraspip install torchData engineers use Keras to prototype and deploy deep learning models quickly. The high-level API reduces boilerplate — defining a model, compiling with a loss and optimizer, and calling `.fit()` with a data pipeline is typically under 20 lines. Keras preprocessing layers are particularly useful for building self-contained models that include their own data transformation logic.
Data engineers building ML data pipelines use PyTorch's `Dataset` and `DataLoader` classes to efficiently feed training data from disk or databases to GPU — defining custom `__getitem__` methods that load, preprocess, and augment data samples. `DataLoader` handles batching, shuffling, and parallel loading transparently.
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