Keras 3: Revolutionizing Deep Learning with Cross-Framework Support for TensorFlow, PyTorch, & JAX скачать в хорошем качестве

Keras 3: Revolutionizing Deep Learning with Cross-Framework Support for TensorFlow, PyTorch, & JAX

🔹 Join the Blog & Follow on social handles for engaging conversations about Software Architecture and Tech.   / cafeio     / akarshverma     / akarshverma   🔹 Capture, research, and manage your Second Brain 🧠 with Heptabase, a visual note-taking tool for learning complex topics: https://get.heptabase.com/19xq4hkjtene Keras 3 is a major update that brings a number of new features and improvements, including: • Support for multiple backends: Keras 3 can now run on top of JAX, TensorFlow, or PyTorch. This means that you can choose the backend that best suits your needs, and you can even switch between backends without having to change your code. • A new distribution API: Keras 3 includes a new distribution API that makes it easy to train models on multiple GPUs or TPUs. This is a major advantage for training large models. • A new stateless API: Keras 3 includes a new stateless API that makes it possible to use Keras layers and models in JAX functions. This is important for using Keras with JAX's functional programming features. • Support for cross-framework data pipelines: Keras 3 can now be used with data pipelines from any framework, including tf.data, PyTorch DataLoader, and NumPy arrays. This makes it easy to use Keras with your existing data pipelines. 0:00 Introduction to Keras 3 and Multi-Framework Machine Learning 6:21 Cross-Compatibility and Integrating Multi-Framework Models 9:02 Data Handling, Model Training, and TensorFlow Distribution 12:52 Conclusion and Call to Action Edited with Gling AI: https://bit.ly/46bGeYv