Keras
Keras is the accessible API for Deep Learning and can run on TensorFlow, PyTorch, or JAX.
Keras is a Python library that makes deep learning accessible: A neural network can be created in a few lines instead of hundreds. Since version 3, the same code can optionally run on three backends: TensorFlow, PyTorch, or JAX.
Well-suited for getting started with the topic and for standard tasks: sales forecasting, text classification, and image recognition with pre-trained models. Its strength is learning speed; your team will have a first working model in days, not weeks.
Those who need full control over every training loop work directly in PyTorch; in research, Keras plays almost no role anymore. The weakness: With unusual architectures, the simplification reaches its limits, and then the work in the backend starts from scratch.
Ideal for developers who are learning ML or building standard models without being deep learning specialists. It remains a top choice as a stepping stone into the field. For companies without developers, the same applies here: first check out ready-made AI APIs like Claude.
Functions
- Flattest learning curve among deep learning tools
- Switching backends without rewriting code since Keras 3
- Used by CERN, NASA, and Waymo
- Standard API within TensorFlow
- Extensive documentation with many tutorials
Core Functions
- Building neural networks with a few lines of Python code
- Three backends to choose from: TensorFlow, PyTorch or JAX
- Pre-trained models via KerasHub
- Ready-made building blocks for layers, training, and evaluation
- Export to the production formats of the respective backend
Links
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Keras
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