keras

Why we included this project

Most Python developers who want to train neural networks without getting lost in tensor mechanics end up using Keras. The 3.x release's real selling point is backend portability: the same model code runs on TensorFlow, JAX, or PyTorch, and switching runtimes is just an environment variable change, not a rewrite of your layers. That flexibility matters for teams that prototype in one framework and later move to another for performance or deployment reasons, and it means custom layers and training loops stay usable across backends. Keras handles the usual deep learning workloads out of the box, from computer vision and NLP to time-series forecasting, and scales from a laptop to multi-GPU and TPU clusters. Whether you're starting a new model project or maintaining existing tf.keras code, it's a solid foundation.