Keras is a deep learning API designed for human beings, not machines. Keras focuses on debugging speed, code elegance & conciseness, maintainability, and deployability.

We recommend a clean python environment for each backend to avoid CUDA version mismatches. As an example, here is how to create a JAX GPU environment with Conda:

Our code examples are short (less than 300 lines of code), focused demonstrations of vertical deep learning workflows. All of our examples are written as Jupyter notebooks and can be run in one click …

Bounding boxes Python & NumPy utilities Bounding boxes utilities Visualization utilities Preprocessing utilities Backend utilities Scikit-Learn API wrappers Keras configuration utilities Keras 3 API …

Welcome to multi-framework machine learning. You're already familiar with the benefits of using Keras — it enables high-velocity development via an obsessive focus on great UX, API design, and …

Our developer guides are deep-dives into specific topics such as layer subclassing, fine-tuning, or model saving. They're one of the best ways to become a Keras expert.

About Keras 3 Keras is a deep learning API written in Python and capable of running on top of either JAX, TensorFlow, or PyTorch. Keras is: Simple – but not simplistic. Keras reduces developer …

Jul 10, 2023 · Introduction Keras 3 is a deep learning framework works with TensorFlow, JAX, and PyTorch interchangeably. This notebook will walk you through key Keras 3 workflows.

Keras Applications are deep learning models that are made available alongside pre-trained weights. These models can be used for prediction, feature extraction, and fine-tuning.

Apr 26, 2020 · Relevant Chapters from Deep Learning with Python Chapter 10: Interpreting what ConvNets learn