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

Keras 3 is a multi-framework deep learning API As a multi-framework API, Keras can be used to develop modular components that are compatible with any framework – JAX, TensorFlow, or PyTorch.

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 …

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 …

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.

Utilities Experiment management utilities Model plotting utilities Structured data preprocessing utilities Tensor utilities Python & NumPy utilities Scikit-Learn API wrappers Keras configuration utilities Keras …

Reinforcement Learning Actor Critic Method Proximal Policy Optimization Deep Q-Learning for Atari Breakout Deep Deterministic Policy Gradient (DDPG)

Jan 30, 2016 · This is a step by step guide to start running deep learning Jupyter notebooks on an AWS GPU instance, while editing the notebooks from anywhere, in your browser.

Nov 7, 2023 · This powerful API introduces a suite of tools enabling data and model parallelism, allowing for efficient scaling of deep learning models on multiple accelerators and hosts.